chore: Release v3.5.3

* fix: Fix installation conflict between onnxruntime and onnxruntime-gpu

* fix: Fix CI, notebooks, type hints, and packaging issues found in audit

* feat: Add new release config

* ci: Automate release pipeline and document release process

.github/workflows/ci.yml

@@ -1,4 +1,4 @@
-name: Build
+name: CI
 
 on:
   push:
@@ -17,10 +17,10 @@ jobs:
     runs-on: ubuntu-latest
     timeout-minutes: 5
     steps:
-      - uses: actions/checkout@v4
-      - uses: actions/setup-python@v5
+      - uses: actions/checkout@v5
+      - uses: actions/setup-python@v6
         with:
-          python-version: "3.10"
+          python-version: "3.11"
       - uses: pre-commit/action@v3.0.1
 
   test:
@@ -35,8 +35,14 @@ jobs:
           # Full Python range on Linux (fastest runner)
           - os: ubuntu-latest
             python-version: "3.10"
+          - os: ubuntu-latest
+            python-version: "3.11"
+          - os: ubuntu-latest
+            python-version: "3.12"
           - os: ubuntu-latest
             python-version: "3.13"
+          - os: ubuntu-latest
+            python-version: "3.14"
           - os: macos-latest
             python-version: "3.13"
           - os: windows-latest
@@ -44,10 +50,10 @@ jobs:
 
     steps:
       - name: Checkout code
-        uses: actions/checkout@v4
+        uses: actions/checkout@v5
 
       - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v5
+        uses: actions/setup-python@v6
         with:
           python-version: ${{ matrix.python-version }}
           cache: "pip"
@@ -55,7 +61,7 @@ jobs:
       - name: Install dependencies
         run: |
           python -m pip install --upgrade pip
-          python -m pip install .[dev]
+          python -m pip install ".[cpu,dev]"
 
       - name: Check ONNX Runtime providers
         run: |
@@ -74,10 +80,10 @@ jobs:
 
     steps:
       - name: Checkout code
-        uses: actions/checkout@v4
+        uses: actions/checkout@v5
 
       - name: Set up Python
-        uses: actions/setup-python@v5
+        uses: actions/setup-python@v6
         with:
           python-version: "3.11"
           cache: "pip"

.github/workflows/docs.yml

@@ -1,8 +1,6 @@
-name: Deploy docs
+name: Deploy Documentation
 
 on:
-  push:
-    branches: [main]
   workflow_dispatch:
 
 permissions:
@@ -12,11 +10,11 @@ jobs:
   deploy:
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v5
         with:
-          fetch-depth: 0  # Fetch full history for git-committers and git-revision-date plugins
+          fetch-depth: 0
 
-      - uses: actions/setup-python@v5
+      - uses: actions/setup-python@v6
         with:
           python-version: "3.11"
 

.github/workflows/pipeline.yml

@@ -0,0 +1,221 @@
+name: Release Pipeline
+
+on:
+  workflow_dispatch:
+    inputs:
+      version:
+        description: 'Version (e.g. 3.6.0, 3.6.0b1, 3.6.0rc1)'
+        required: true
+
+concurrency:
+  group: pipeline
+  cancel-in-progress: false
+
+jobs:
+  validate:
+    runs-on: ubuntu-latest
+    timeout-minutes: 5
+    outputs:
+      is_prerelease: ${{ steps.prerelease.outputs.is_prerelease }}
+
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v5
+
+      - name: Set up Python
+        uses: actions/setup-python@v6
+        with:
+          python-version: "3.11"
+
+      - name: Validate version (PEP 440)
+        run: |
+          python - <<'EOF'
+          import re, sys
+          v = "${{ inputs.version }}"
+          if not re.fullmatch(r'\d+\.\d+\.\d+((a|b|rc)\d+|\.dev\d+)?', v):
+              print(f"Invalid version: {v}")
+              print("Expected forms: 3.6.0, 3.6.0a1, 3.6.0b1, 3.6.0rc1, 3.6.0.dev1")
+              sys.exit(1)
+          EOF
+
+      - name: Check tag does not exist
+        run: |
+          if git rev-parse "v${{ inputs.version }}" >/dev/null 2>&1; then
+            echo "Tag v${{ inputs.version }} already exists."
+            exit 1
+          fi
+
+      - name: Detect pre-release
+        id: prerelease
+        run: |
+          if [[ "${{ inputs.version }}" =~ (a|b|rc|\.dev)[0-9]+ ]]; then
+            echo "is_prerelease=true" >> $GITHUB_OUTPUT
+          else
+            echo "is_prerelease=false" >> $GITHUB_OUTPUT
+          fi
+
+  test:
+    runs-on: ubuntu-latest
+    timeout-minutes: 15
+    needs: validate
+
+    strategy:
+      fail-fast: false
+      matrix:
+        python-version: ["3.10", "3.11", "3.12", "3.13", "3.14"]
+
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v5
+
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v6
+        with:
+          python-version: ${{ matrix.python-version }}
+          cache: 'pip'
+
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          python -m pip install ".[cpu,dev]"
+
+      - name: Run tests
+        run: pytest -v --tb=short
+
+  release:
+    runs-on: ubuntu-latest
+    timeout-minutes: 5
+    needs: test
+    permissions:
+      contents: write
+
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v5
+        with:
+          fetch-depth: 0
+          token: ${{ secrets.RELEASE_TOKEN }}
+
+      - name: Set up Python
+        uses: actions/setup-python@v6
+        with:
+          python-version: "3.11"
+
+      - name: Update pyproject.toml
+        run: |
+          python - <<'EOF'
+          import re, pathlib
+          p = pathlib.Path('pyproject.toml')
+          text = p.read_text()
+          new = re.sub(r'^version\s*=\s*".*"', f'version = "${{ inputs.version }}"', text, count=1, flags=re.M)
+          if new == text:
+              raise SystemExit("Failed to update version in pyproject.toml")
+          p.write_text(new)
+          EOF
+
+      - name: Update uniface/__init__.py
+        run: |
+          python - <<'EOF'
+          import re, pathlib
+          p = pathlib.Path('uniface/__init__.py')
+          text = p.read_text()
+          new = re.sub(r"^__version__\s*=\s*'.*'", f"__version__ = '${{ inputs.version }}'", text, count=1, flags=re.M)
+          if new == text:
+              raise SystemExit("Failed to update __version__ in uniface/__init__.py")
+          p.write_text(new)
+          EOF
+
+      - name: Commit, tag, push
+        run: |
+          git config user.name "github-actions[bot]"
+          git config user.email "41898282+github-actions[bot]@users.noreply.github.com"
+          git add pyproject.toml uniface/__init__.py
+          git commit -m "chore: Release v${{ inputs.version }}"
+          git tag "v${{ inputs.version }}"
+          git push origin HEAD:${{ github.ref_name }}
+          git push origin "v${{ inputs.version }}"
+
+  publish:
+    runs-on: ubuntu-latest
+    timeout-minutes: 10
+    needs: [validate, release]
+    permissions:
+      contents: write
+      id-token: write
+    environment:
+      name: pypi
+      url: https://pypi.org/project/uniface/
+
+    steps:
+      - name: Checkout tag
+        uses: actions/checkout@v5
+        with:
+          ref: v${{ inputs.version }}
+
+      - name: Set up Python
+        uses: actions/setup-python@v6
+        with:
+          python-version: "3.11"
+          cache: 'pip'
+
+      - name: Install build tools
+        run: |
+          python -m pip install --upgrade pip
+          python -m pip install build twine
+
+      - name: Build package
+        run: python -m build
+
+      - name: Check package
+        run: twine check dist/*
+
+      - name: Publish to PyPI
+        env:
+          TWINE_USERNAME: __token__
+          TWINE_PASSWORD: ${{ secrets.PYPI_API_TOKEN }}
+        run: twine upload dist/*
+
+      - name: Create GitHub Release
+        uses: softprops/action-gh-release@v1
+        with:
+          tag_name: v${{ inputs.version }}
+          files: dist/*
+          generate_release_notes: true
+          prerelease: ${{ needs.validate.outputs.is_prerelease }}
+
+  docs:
+    runs-on: ubuntu-latest
+    timeout-minutes: 10
+    needs: [validate, publish]
+    if: needs.validate.outputs.is_prerelease == 'false'
+    permissions:
+      contents: write
+
+    steps:
+      - name: Checkout tag
+        uses: actions/checkout@v5
+        with:
+          ref: v${{ inputs.version }}
+          fetch-depth: 0
+
+      - name: Set up Python
+        uses: actions/setup-python@v6
+        with:
+          python-version: "3.11"
+
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install mkdocs-material pymdown-extensions mkdocs-git-committers-plugin-2 mkdocs-git-revision-date-localized-plugin
+
+      - name: Build docs
+        env:
+          MKDOCS_GIT_COMMITTERS_APIKEY: ${{ secrets.MKDOCS_GIT_COMMITTERS_APIKEY }}
+        run: mkdocs build --strict
+
+      - name: Deploy to GitHub Pages
+        uses: peaceiris/actions-gh-pages@v4
+        with:
+          github_token: ${{ secrets.GITHUB_TOKEN }}
+          publish_dir: ./site
+          destination_dir: docs

.github/workflows/publish.yml

@@ -1,119 +0,0 @@
-name: Publish to PyPI
-
-on:
-  push:
-    tags:
-      - "v*.*.*"  # Trigger only on version tags like v0.1.9
-
-concurrency:
-  group: ${{ github.workflow }}-${{ github.ref }}
-  cancel-in-progress: true
-
-jobs:
-  validate:
-    runs-on: ubuntu-latest
-    timeout-minutes: 5
-    outputs:
-      version: ${{ steps.get_version.outputs.version }}
-      tag_version: ${{ steps.get_version.outputs.tag_version }}
-
-    steps:
-      - name: Checkout code
-        uses: actions/checkout@v4
-
-      - name: Set up Python
-        uses: actions/setup-python@v5
-        with:
-          python-version: "3.11"  # Needs 3.11+ for tomllib
-
-      - name: Get version from tag and pyproject.toml
-        id: get_version
-        run: |
-          TAG_VERSION=${GITHUB_REF#refs/tags/v}
-          echo "tag_version=$TAG_VERSION" >> $GITHUB_OUTPUT
-
-          PYPROJECT_VERSION=$(python -c "import tomllib; print(tomllib.load(open('pyproject.toml','rb'))['project']['version'])")
-          echo "version=$PYPROJECT_VERSION" >> $GITHUB_OUTPUT
-
-          echo "Tag version: v$TAG_VERSION"
-          echo "pyproject.toml version: $PYPROJECT_VERSION"
-
-      - name: Verify version match
-        run: |
-          if [ "${{ steps.get_version.outputs.tag_version }}" != "${{ steps.get_version.outputs.version }}" ]; then
-            echo "Error: Tag version (${{ steps.get_version.outputs.tag_version }}) does not match pyproject.toml version (${{ steps.get_version.outputs.version }})"
-            exit 1
-          fi
-          echo "Version validation passed: ${{ steps.get_version.outputs.version }}"
-
-  test:
-    runs-on: ubuntu-latest
-    timeout-minutes: 15
-    needs: validate
-
-    strategy:
-      fail-fast: false
-      matrix:
-        python-version: ["3.10", "3.13"]
-
-    steps:
-      - name: Checkout code
-        uses: actions/checkout@v4
-
-      - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v5
-        with:
-          python-version: ${{ matrix.python-version }}
-          cache: 'pip'
-
-      - name: Install dependencies
-        run: |
-          python -m pip install --upgrade pip
-          python -m pip install .[dev]
-
-      - name: Run tests
-        run: pytest -v
-
-  publish:
-    runs-on: ubuntu-latest
-    timeout-minutes: 10
-    needs: [validate, test]
-    permissions:
-      contents: write
-      id-token: write
-    environment:
-      name: pypi
-      url: https://pypi.org/project/uniface/
-
-    steps:
-      - name: Checkout code
-        uses: actions/checkout@v4
-
-      - name: Set up Python
-        uses: actions/setup-python@v5
-        with:
-          python-version: "3.10"
-          cache: 'pip'
-
-      - name: Install build tools
-        run: |
-          python -m pip install --upgrade pip
-          python -m pip install build twine
-
-      - name: Build package
-        run: python -m build
-
-      - name: Check package
-        run: twine check dist/*
-
-      - name: Publish to PyPI
-        env:
-          TWINE_USERNAME: __token__
-          TWINE_PASSWORD: ${{ secrets.PYPI_API_TOKEN }}
-        run: twine upload dist/*
-
-      - name: Create GitHub Release
-        uses: softprops/action-gh-release@v1
-        with:
-          files: dist/*
-          generate_release_notes: true

.pre-commit-config.yaml

@@ -18,6 +18,13 @@ repos:
       - id: debug-statements
       - id: check-ast
 
+  # Strip Jupyter notebook outputs
+  - repo: https://github.com/kynan/nbstripout
+    rev: 0.9.1
+    hooks:
+      - id: nbstripout
+        files: ^examples/
+
   # Ruff - Fast Python linter and formatter
   - repo: https://github.com/astral-sh/ruff-pre-commit
     rev: v0.14.10

AGENTS.md

@@ -0,0 +1,6 @@
+<!-- Cursor agent instructions — shared with CLAUDE.md -->
+<!-- See CLAUDE.md for full project instructions for AI coding agents. -->
+
+# AGENTS.md
+
+Please read and follow all instructions in [CLAUDE.md](./CLAUDE.md).

CLAUDE.md

@@ -0,0 +1,81 @@
+# CLAUDE.md
+
+Project instructions for AI coding agents.
+
+## Project Overview
+
+UniFace is a Python library for face detection, recognition, tracking, landmark analysis, face parsing, gaze estimation, age/gender detection. It uses ONNX Runtime for inference.
+
+## Code Style
+
+- Python 3.10+ with type hints
+- Line length: 120
+- Single quotes for strings, double quotes for docstrings
+- Google-style docstrings
+- Formatter/linter: Ruff (config in `pyproject.toml`)
+- Run `ruff format .` and `ruff check . --fix` before committing
+
+## Commit Messages
+
+Follow [Conventional Commits](https://www.conventionalcommits.org/) with a **capitalized** description:
+
+```
+<type>: <Capitalized short description>
+```
+
+Types: `feat`, `fix`, `docs`, `style`, `refactor`, `perf`, `test`, `build`, `ci`, `chore`
+
+Examples:
+- `feat: Add gaze estimation model`
+- `fix: Correct bounding box scaling for non-square images`
+- `ci: Add nbstripout pre-commit hook`
+- `docs: Update installation instructions`
+- `refactor: Unify attribute/detector base classes`
+
+## Testing
+
+```bash
+pytest -v --tb=short
+```
+
+Tests live in `tests/`. Run the full suite before submitting changes.
+
+## Pre-commit
+
+Pre-commit hooks handle formatting, linting, security checks, and notebook output stripping. Always run:
+
+```bash
+pre-commit install
+pre-commit run --all-files
+```
+
+## Project Structure
+
+```
+uniface/            # Main package
+  detection/        # Face detection models (SCRFD, RetinaFace, YOLOv5, YOLOv8)
+  recognition/      # Face recognition/verification (AdaFace, ArcFace, EdgeFace, MobileFace, SphereFace)
+  landmark/         # Facial landmark models
+  tracking/         # Object tracking (ByteTrack)
+  parsing/          # Face parsing/segmentation (BiSeNet, XSeg)
+  gaze/             # Gaze estimation
+  headpose/         # Head pose estimation
+  attribute/        # Age, gender, emotion detection
+  spoofing/         # Anti-spoofing (MiniFASNet)
+  privacy/          # Face anonymization
+  stores/           # Vector stores (FAISS)
+  constants.py      # Model weight URLs and checksums
+  model_store.py    # Model download/cache management
+  analyzer.py       # High-level FaceAnalyzer API
+  types.py          # Shared type definitions
+tests/              # Unit tests
+examples/           # Jupyter notebooks (outputs are auto-stripped)
+docs/               # MkDocs documentation
+```
+
+## Key Conventions
+
+- New models: add class in submodule, register weights in `constants.py`, export in `__init__.py`
+- Dependencies: managed in `pyproject.toml`
+- All ONNX models are downloaded on demand with SHA256 verification
+- Do not commit notebook outputs; `nbstripout` pre-commit hook handles this

CONTRIBUTING.md

@@ -184,6 +184,48 @@ Example notebooks demonstrating library usage:
 | Face Parsing       | [06_face_parsing.ipynb](examples/06_face_parsing.ipynb)             |
 | Face Anonymization | [07_face_anonymization.ipynb](examples/07_face_anonymization.ipynb) |
 | Gaze Estimation    | [08_gaze_estimation.ipynb](examples/08_gaze_estimation.ipynb)       |
+| Face Segmentation  | [09_face_segmentation.ipynb](examples/09_face_segmentation.ipynb)   |
+| Face Vector Store  | [10_face_vector_store.ipynb](examples/10_face_vector_store.ipynb)   |
+| Head Pose Estimation | [11_head_pose_estimation.ipynb](examples/11_head_pose_estimation.ipynb) |
+
+## Release Process
+
+Releases are fully automated via GitHub Actions. Only maintainers with branch-protection bypass privileges on `main` can trigger a release.
+
+### Cutting a release
+
+1. Go to **Actions → Release Pipeline → Run workflow** on GitHub.
+2. Enter the version following [PEP 440](https://peps.python.org/pep-0440/):
+   - Stable: `0.7.0`, `1.0.0`
+   - Pre-release: `0.7.0rc1`, `0.7.0b1`, `0.7.0a1`, `0.7.0.dev1`
+3. Click **Run workflow**.
+
+### What happens automatically
+
+The `Release Pipeline` workflow runs all stages in sequence:
+
+1. **Validate** — checks the version string against PEP 440 and confirms the tag does not already exist.
+2. **Test** — runs the test suite on Python 3.10–3.14.
+3. **Release** — updates `pyproject.toml` and `uniface/__init__.py`, commits `chore: Release vX.Y.Z` to `main`, creates and pushes tag `vX.Y.Z`.
+4. **Publish** — builds the package, uploads to PyPI, and creates a GitHub Release (flagged as pre-release for `a`/`b`/`rc`/`.dev` versions).
+5. **Deploy docs** — runs only for **stable** versions. Pre-releases do not update the live documentation site.
+
+### Verifying a release
+
+- PyPI: <https://pypi.org/project/uniface/>
+- GitHub Releases: <https://github.com/yakhyo/uniface/releases>
+- Docs (stable only): <https://yakhyo.github.io/uniface/>
+
+### Installing a pre-release
+
+End users can opt in to pre-releases with the `--pre` flag:
+
+```bash
+pip install uniface --pre                # latest pre-release
+pip install uniface==0.7.0rc1            # specific pre-release
+```
+
+Without `--pre`, `pip install uniface` always resolves to the latest stable version.
 
 ## Questions?
 

README.md

@@ -1,4 +1,4 @@
-<h1 align="center">UniFace: All-in-One Face Analysis Library</h1>
+<h1 align="center">UniFace: A Unified Face Analysis Library for Python</h1>
 
 <div align="center">
 
@@ -14,53 +14,90 @@
 </div>
 
 <div align="center">
-    <img src="https://raw.githubusercontent.com/yakhyo/uniface/main/.github/logos/uniface_rounded_q80.webp" width="90%" alt="UniFace - All-in-One Open-Source Face Analysis Library">
+    <img src="https://raw.githubusercontent.com/yakhyo/uniface/main/.github/logos/uniface_rounded_q80.webp" width="90%" alt="UniFace - A Unified Face Analysis Library for Python">
 </div>
 
 ---
 
-**UniFace** is a lightweight, production-ready face analysis library built on ONNX Runtime. It provides high-performance face detection, recognition, landmark detection, face parsing, gaze estimation, and attribute analysis with hardware acceleration support across platforms.
+**UniFace** is a lightweight, production-ready Python library for face detection, recognition, tracking, landmark analysis, face parsing, gaze estimation, and face attributes.
 
 ---
 
 ## Features
 
 - **Face Detection** — RetinaFace, SCRFD, YOLOv5-Face, and YOLOv8-Face with 5-point landmarks
-- **Face Recognition** — ArcFace, MobileFace, and SphereFace embeddings
+- **Face Recognition** — AdaFace, ArcFace, EdgeFace, MobileFace, and SphereFace embeddings
 - **Face Tracking** — Multi-object tracking with [BYTETracker](https://github.com/yakhyo/bytetrack-tracker) for persistent IDs across video frames
 - **Facial Landmarks** — 106-point landmark localization module (separate from 5-point detector landmarks)
 - **Face Parsing** — BiSeNet semantic segmentation (19 classes), XSeg face masking
+- **Portrait Matting** — Trimap-free alpha matte with MODNet (background removal, green screen, compositing)
 - **Gaze Estimation** — Real-time gaze direction with MobileGaze
+- **Head Pose Estimation** — 3D head orientation (pitch, yaw, roll) with 6D rotation representation
 - **Attribute Analysis** — Age, gender, race (FairFace), and emotion
-- **Vector Indexing** — FAISS-backed embedding store for fast multi-identity search
+- **Vector Store** — FAISS-backed embedding store for fast multi-identity search
 - **Anti-Spoofing** — Face liveness detection with MiniFASNet
 - **Face Anonymization** — 5 blur methods for privacy protection
 - **Hardware Acceleration** — ARM64 (Apple Silicon), CUDA (NVIDIA), CPU
 
 ---
 
+## Visual Examples
+
+<table>
+  <tr>
+    <td align="center"><b>Face Detection</b><br><img src="https://raw.githubusercontent.com/yakhyo/uniface/main/assets/demos/detection.jpg" width="100%"></td>
+    <td align="center"><b>Gaze Estimation</b><br><img src="https://raw.githubusercontent.com/yakhyo/uniface/main/assets/demos/gaze.jpg" width="100%"></td>
+  </tr>
+  <tr>
+    <td align="center"><b>Head Pose Estimation</b><br><img src="https://raw.githubusercontent.com/yakhyo/uniface/main/assets/demos/headpose.jpg" width="100%"></td>
+    <td align="center"><b>Age &amp; Gender</b><br><img src="https://raw.githubusercontent.com/yakhyo/uniface/main/assets/demos/age_gender.jpg" width="100%"></td>
+  </tr>
+  <tr>
+    <td align="center" colspan="2"><b>Face Verification</b><br><img src="https://raw.githubusercontent.com/yakhyo/uniface/main/assets/demos/verification.jpg" width="80%"></td>
+  </tr>
+  <tr>
+    <td align="center" colspan="2"><b>106-Point Landmarks</b><br><img src="https://raw.githubusercontent.com/yakhyo/uniface/main/assets/demos/landmarks.jpg" width="36%"></td>
+  </tr>
+  <tr>
+    <td align="center" colspan="2"><b>Face Parsing</b><br><img src="https://raw.githubusercontent.com/yakhyo/uniface/main/assets/demos/parsing.jpg" width="80%"></td>
+  </tr>
+  <tr>
+    <td align="center" colspan="2"><b>Face Segmentation</b><br><img src="https://raw.githubusercontent.com/yakhyo/uniface/main/assets/demos/segmentation.jpg" width="80%"></td>
+  </tr>
+  <tr>
+    <td align="center" colspan="2"><b>Portrait Matting</b><br><img src="https://raw.githubusercontent.com/yakhyo/uniface/main/assets/demos/matting.jpg" width="100%"></td>
+  </tr>
+  <tr>
+    <td align="center" colspan="2"><b>Face Anonymization</b><br><img src="https://raw.githubusercontent.com/yakhyo/uniface/main/assets/demos/anonymization.jpg" width="100%"></td>
+  </tr>
+</table>
+
+---
+
 ## Installation
 
-**Standard installation**
+**CPU / Apple Silicon**
 
 ```bash
-pip install uniface
+pip install uniface[cpu]
 ```
 
-**GPU support (CUDA)**
+**GPU support (NVIDIA CUDA)**
 
 ```bash
 pip install uniface[gpu]
 ```
 
+> **Why separate extras?** `onnxruntime` and `onnxruntime-gpu` conflict when both are installed — they own the same Python namespace. Installing only the extra you need prevents that conflict entirely.
+
 **From source (latest version)**
 
 ```bash
 git clone https://github.com/yakhyo/uniface.git
-cd uniface && pip install -e .
+cd uniface && pip install -e ".[cpu]"   # or .[gpu] for CUDA
 ```
 
-**FAISS vector indexing**
+**FAISS vector store**
 
 ```bash
 pip install faiss-cpu   # or faiss-gpu for CUDA
@@ -126,14 +163,10 @@ for face in faces:
 
 ```python
 import cv2
-from uniface.analyzer import FaceAnalyzer
-from uniface.detection import RetinaFace
-from uniface.recognition import ArcFace
+from uniface import FaceAnalyzer
 
-detector = RetinaFace()
-recognizer = ArcFace()
-
-analyzer = FaceAnalyzer(detector, recognizer=recognizer)
+# Zero-config: uses SCRFD (500M) + ArcFace (MobileNet) by default
+analyzer = FaceAnalyzer()
 
 image = cv2.imread("photo.jpg")
 if image is None:
@@ -145,19 +178,63 @@ for face in faces:
     print(face.bbox, face.embedding.shape if face.embedding is not None else None)
 ```
 
----
-
-## Execution Providers (ONNX Runtime)
+With attributes:
 
 ```python
-from uniface.detection import RetinaFace
+from uniface import FaceAnalyzer, AgeGender
 
-# Force CPU-only inference
-detector = RetinaFace(providers=["CPUExecutionProvider"])
+analyzer = FaceAnalyzer(attributes=[AgeGender()])
+faces = analyzer.analyze(image)
+
+for face in faces:
+    print(f"{face.sex}, {face.age}y, embedding={face.embedding.shape}")
 ```
 
-See more in the docs:
-https://yakhyo.github.io/uniface/concepts/execution-providers/
+---
+
+## Example (Portrait Matting)
+
+```python
+import cv2
+import numpy as np
+from uniface.matting import MODNet
+
+matting = MODNet()
+
+image = cv2.imread("portrait.jpg")
+matte = matting.predict(image)  # (H, W) float32 in [0, 1]
+
+# Transparent PNG
+rgba = cv2.cvtColor(image, cv2.COLOR_BGR2BGRA)
+rgba[:, :, 3] = (matte * 255).astype(np.uint8)
+cv2.imwrite("transparent.png", rgba)
+
+# Green screen
+matte_3ch = matte[:, :, np.newaxis]
+bg = np.full_like(image, (0, 177, 64), dtype=np.uint8)
+result = (image * matte_3ch + bg * (1 - matte_3ch)).astype(np.uint8)
+cv2.imwrite("green_screen.jpg", result)
+```
+
+---
+
+## Jupyter Notebooks
+
+| Example | Colab | Description |
+|---------|:-----:|-------------|
+| [01_face_detection.ipynb](examples/01_face_detection.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/yakhyo/uniface/blob/main/examples/01_face_detection.ipynb) | Face detection and landmarks |
+| [02_face_alignment.ipynb](examples/02_face_alignment.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/yakhyo/uniface/blob/main/examples/02_face_alignment.ipynb) | Face alignment for recognition |
+| [03_face_verification.ipynb](examples/03_face_verification.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/yakhyo/uniface/blob/main/examples/03_face_verification.ipynb) | Compare faces for identity |
+| [04_face_search.ipynb](examples/04_face_search.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/yakhyo/uniface/blob/main/examples/04_face_search.ipynb) | Find a person in group photos |
+| [05_face_analyzer.ipynb](examples/05_face_analyzer.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/yakhyo/uniface/blob/main/examples/05_face_analyzer.ipynb) | Unified face analysis |
+| [06_face_parsing.ipynb](examples/06_face_parsing.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/yakhyo/uniface/blob/main/examples/06_face_parsing.ipynb) | Semantic face segmentation |
+| [07_face_anonymization.ipynb](examples/07_face_anonymization.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/yakhyo/uniface/blob/main/examples/07_face_anonymization.ipynb) | Privacy-preserving blur |
+| [08_gaze_estimation.ipynb](examples/08_gaze_estimation.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/yakhyo/uniface/blob/main/examples/08_gaze_estimation.ipynb) | Gaze direction estimation |
+| [09_face_segmentation.ipynb](examples/09_face_segmentation.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/yakhyo/uniface/blob/main/examples/09_face_segmentation.ipynb) | Face segmentation with XSeg |
+| [10_face_vector_store.ipynb](examples/10_face_vector_store.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/yakhyo/uniface/blob/main/examples/10_face_vector_store.ipynb) | FAISS-backed face database |
+| [11_head_pose_estimation.ipynb](examples/11_head_pose_estimation.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/yakhyo/uniface/blob/main/examples/11_head_pose_estimation.ipynb) | Head pose estimation (pitch, yaw, roll) |
+| [12_face_recognition.ipynb](examples/12_face_recognition.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/yakhyo/uniface/blob/main/examples/12_face_recognition.ipynb) | Standalone face recognition pipeline |
+| [13_portrait_matting.ipynb](examples/13_portrait_matting.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/yakhyo/uniface/blob/main/examples/13_portrait_matting.ipynb) | Portrait matting with MODNet |
 
 ---
 
@@ -176,6 +253,20 @@ Full documentation: https://yakhyo.github.io/uniface/
 
 ---
 
+## Execution Providers (ONNX Runtime)
+
+```python
+from uniface.detection import RetinaFace
+
+# Force CPU-only inference
+detector = RetinaFace(providers=["CPUExecutionProvider"])
+```
+
+See more in the docs:
+https://yakhyo.github.io/uniface/concepts/execution-providers/
+
+---
+
 ## Datasets
 
 | Task | Training Dataset | Models |
@@ -184,7 +275,9 @@ Full documentation: https://yakhyo.github.io/uniface/
 | Recognition | MS1MV2 | MobileFace, SphereFace |
 | Recognition | WebFace600K | ArcFace |
 | Recognition | WebFace4M / 12M | AdaFace |
+| Recognition | MS1MV2 | EdgeFace |
 | Gaze | Gaze360 | MobileGaze |
+| Head Pose | 300W-LP | HeadPose (ResNet, MobileNet) |
 | Parsing | CelebAMask-HQ | BiSeNet |
 | Attributes | CelebA, FairFace, AffectNet | AgeGender, FairFace, Emotion |
 
@@ -192,23 +285,6 @@ Full documentation: https://yakhyo.github.io/uniface/
 
 ---
 
-## Jupyter Notebooks
-
-| Example | Colab | Description |
-|---------|:-----:|-------------|
-| [01_face_detection.ipynb](examples/01_face_detection.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/yakhyo/uniface/blob/main/examples/01_face_detection.ipynb) | Face detection and landmarks |
-| [02_face_alignment.ipynb](examples/02_face_alignment.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/yakhyo/uniface/blob/main/examples/02_face_alignment.ipynb) | Face alignment for recognition |
-| [03_face_verification.ipynb](examples/03_face_verification.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/yakhyo/uniface/blob/main/examples/03_face_verification.ipynb) | Compare faces for identity |
-| [04_face_search.ipynb](examples/04_face_search.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/yakhyo/uniface/blob/main/examples/04_face_search.ipynb) | Find a person in group photos |
-| [05_face_analyzer.ipynb](examples/05_face_analyzer.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/yakhyo/uniface/blob/main/examples/05_face_analyzer.ipynb) | All-in-one analysis |
-| [06_face_parsing.ipynb](examples/06_face_parsing.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/yakhyo/uniface/blob/main/examples/06_face_parsing.ipynb) | Semantic face segmentation |
-| [07_face_anonymization.ipynb](examples/07_face_anonymization.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/yakhyo/uniface/blob/main/examples/07_face_anonymization.ipynb) | Privacy-preserving blur |
-| [08_gaze_estimation.ipynb](examples/08_gaze_estimation.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/yakhyo/uniface/blob/main/examples/08_gaze_estimation.ipynb) | Gaze direction estimation |
-| [09_face_segmentation.ipynb](examples/09_face_segmentation.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/yakhyo/uniface/blob/main/examples/09_face_segmentation.ipynb) | Face segmentation with XSeg |
-| [10_face_vector_store.ipynb](examples/10_face_vector_store.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/yakhyo/uniface/blob/main/examples/10_face_vector_store.ipynb) | FAISS-backed face database |
-
----
-
 ## Licensing and Model Usage
 
 UniFace is MIT-licensed, but several pretrained models carry their own licenses.
@@ -231,9 +307,12 @@ If you plan commercial use, verify model license compatibility.
 | Detection | [yolov8-face-onnx-inference](https://github.com/yakhyo/yolov8-face-onnx-inference) | - | YOLOv8-Face ONNX Inference |
 | Tracking | [bytetrack-tracker](https://github.com/yakhyo/bytetrack-tracker) | - | BYTETracker Multi-Object Tracking |
 | Recognition | [face-recognition](https://github.com/yakhyo/face-recognition) | ✓ | MobileFace, SphereFace Training |
+| Recognition | [edgeface-onnx](https://github.com/yakhyo/edgeface-onnx) | - | EdgeFace ONNX Inference |
 | Parsing | [face-parsing](https://github.com/yakhyo/face-parsing) | ✓ | BiSeNet Face Parsing |
 | Parsing | [face-segmentation](https://github.com/yakhyo/face-segmentation) | - | XSeg Face Segmentation |
 | Gaze | [gaze-estimation](https://github.com/yakhyo/gaze-estimation) | ✓ | MobileGaze Training |
+| Head Pose | [head-pose-estimation](https://github.com/yakhyo/head-pose-estimation) | ✓ | Head Pose Training (6DRepNet-style) |
+| Matting | [modnet](https://github.com/yakhyo/modnet) | - | MODNet Portrait Matting |
 | Anti-Spoofing | [face-anti-spoofing](https://github.com/yakhyo/face-anti-spoofing) | - | MiniFASNet Inference |
 | Attributes | [fairface-onnx](https://github.com/yakhyo/fairface-onnx) | - | FairFace ONNX Inference |
 
@@ -257,3 +336,6 @@ Questions or feedback:
 ## License
 
 This project is licensed under the [MIT License](LICENSE).
+
+> **Disclaimer:** This project is not affiliated with or related to
+> [Uniface](https://uniface.com/) by Rocket Software.

docs/concepts/execution-providers.md

@@ -39,16 +39,20 @@ recognizer = ArcFace(providers=['CPUExecutionProvider'])
 detector = RetinaFace(providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])
 ```
 
-All model classes accept the `providers` parameter:
+All **ONNX-based** model classes accept the `providers` parameter:
 
 - Detection: `RetinaFace`, `SCRFD`, `YOLOv5Face`, `YOLOv8Face`
 - Recognition: `ArcFace`, `AdaFace`, `MobileFace`, `SphereFace`
 - Landmarks: `Landmark106`
 - Gaze: `MobileGaze`
-- Parsing: `BiSeNet`
+- Parsing: `BiSeNet`, `XSeg`
 - Attributes: `AgeGender`, `FairFace`
 - Anti-Spoofing: `MiniFASNet`
 
+!!! note "Non-ONNX components"
+    - **Emotion** uses TorchScript and selects its device automatically (`mps` / `cuda` / `cpu`). It does **not** accept the `providers` parameter.
+    - **BlurFace** is a pure OpenCV utility and does not load any model.
+
 ---
 
 ## Check Available Providers
@@ -89,7 +93,7 @@ print("Available providers:", providers)
 No additional setup required. ARM64 optimizations are built into `onnxruntime`:
 
 ```bash
-pip install uniface
+pip install uniface[cpu]
 ```
 
 Verify ARM64:
@@ -106,7 +110,7 @@ python -c "import platform; print(platform.machine())"
 
 ### NVIDIA GPU (CUDA)
 
-Install with GPU support:
+Install with GPU support (this installs `onnxruntime-gpu`, which already includes CPU fallback):
 
 ```bash
 pip install uniface[gpu]
@@ -136,7 +140,7 @@ else:
 CPU execution is always available:
 
 ```bash
-pip install uniface
+pip install uniface[cpu]
 ```
 
 Works on all platforms without additional configuration.
@@ -211,7 +215,7 @@ for image_path in image_paths:
 
 3. Reinstall with GPU support:
    ```bash
-   pip uninstall onnxruntime onnxruntime-gpu
+   pip uninstall onnxruntime onnxruntime-gpu -y
    pip install uniface[gpu]
    ```
 

docs/concepts/inputs-outputs.md

@@ -106,6 +106,27 @@ print(f"Yaw: {np.degrees(result.yaw):.1f}°")
 
 ---
 
+### HeadPoseResult
+
+```python
+@dataclass(frozen=True)
+class HeadPoseResult:
+    pitch: float  # Rotation around X-axis (degrees), + = looking down
+    yaw: float    # Rotation around Y-axis (degrees), + = looking right
+    roll: float   # Rotation around Z-axis (degrees), + = tilting clockwise
+```
+
+**Usage:**
+
+```python
+result = head_pose.estimate(face_crop)
+print(f"Pitch: {result.pitch:.1f}°")
+print(f"Yaw: {result.yaw:.1f}°")
+print(f"Roll: {result.roll:.1f}°")
+```
+
+---
+
 ### SpoofingResult
 
 ```python
@@ -144,11 +165,11 @@ class AttributeResult:
 
 ```python
 # AgeGender model
-result = age_gender.predict(image, face.bbox)
+result = age_gender.predict(image, face)
 print(f"{result.sex}, {result.age} years old")
 
 # FairFace model
-result = fairface.predict(image, face.bbox)
+result = fairface.predict(image, face)
 print(f"{result.sex}, {result.age_group}, {result.race}")
 ```
 
@@ -171,7 +192,7 @@ Face recognition models return normalized 512-dimensional embeddings:
 
 ```python
 embedding = recognizer.get_normalized_embedding(image, landmarks)
-print(f"Shape: {embedding.shape}")  # (1, 512)
+print(f"Shape: {embedding.shape}")  # (512,)
 print(f"Norm: {np.linalg.norm(embedding):.4f}")  # ~1.0
 ```
 

docs/concepts/overview.md

@@ -23,8 +23,10 @@ graph TB
         LMK[Landmarks]
         ATTR[Attributes]
         GAZE[Gaze]
+        HPOSE[Head Pose]
         PARSE[Parsing]
         SPOOF[Anti-Spoofing]
+        MATT[Matting]
         PRIV[Privacy]
     end
 
@@ -32,7 +34,7 @@ graph TB
         TRK[BYTETracker]
     end
 
-    subgraph Indexing
+    subgraph Stores
         IDX[FAISS Vector Store]
     end
 
@@ -41,10 +43,12 @@ graph TB
     end
 
     IMG --> DET
+    IMG --> MATT
     DET --> REC
     DET --> LMK
     DET --> ATTR
     DET --> GAZE
+    DET --> HPOSE
     DET --> PARSE
     DET --> SPOOF
     DET --> PRIV
@@ -60,16 +64,14 @@ graph TB
 
 ## Design Principles
 
-### 1. ONNX-First
+### 1. Cross-Platform Inference
 
-UniFace runs inference primarily via ONNX Runtime for core components:
+UniFace uses portable model runtimes to provide consistent inference across macOS, Linux, and Windows. Most core components run through ONNX Runtime, while optional components may use PyTorch where appropriate.
 
 - **Cross-platform**: Same models work on macOS, Linux, Windows
 - **Hardware acceleration**: Automatic selection of optimal provider
 - **Production-ready**: No Python-only dependencies for inference
 
-Some optional components (e.g., emotion TorchScript, torchvision NMS) require PyTorch.
-
 ### 2. Minimal Dependencies
 
 Core dependencies are kept minimal:
@@ -113,16 +115,18 @@ def detect(self, image: np.ndarray) -> list[Face]:
 ```
 uniface/
 ├── detection/      # Face detection (RetinaFace, SCRFD, YOLOv5Face, YOLOv8Face)
-├── recognition/    # Face recognition (AdaFace, ArcFace, MobileFace, SphereFace)
+├── recognition/    # Face recognition (AdaFace, ArcFace, EdgeFace, MobileFace, SphereFace)
 ├── tracking/       # Multi-object tracking (BYTETracker)
 ├── landmark/       # 106-point landmarks
 ├── attribute/      # Age, gender, emotion, race
 ├── parsing/        # Face semantic segmentation
+├── matting/        # Portrait matting (MODNet)
 ├── gaze/           # Gaze estimation
+├── headpose/       # Head pose estimation
 ├── spoofing/       # Anti-spoofing
 ├── privacy/        # Face anonymization
-├── indexing/       # Vector indexing (FAISS)
-├── types.py        # Dataclasses (Face, GazeResult, etc.)
+├── stores/         # Vector stores (FAISS)
+├── types.py        # Dataclasses (Face, GazeResult, HeadPoseResult, etc.)
 ├── constants.py    # Model weights and URLs
 ├── model_store.py  # Model download and caching
 ├── onnx_utils.py   # ONNX Runtime utilities
@@ -158,7 +162,7 @@ for face in faces:
     embedding = recognizer.get_normalized_embedding(image, face.landmarks)
 
     # Attributes
-    attrs = age_gender.predict(image, face.bbox)
+    attrs = age_gender.predict(image, face)
 
     print(f"Face: {attrs.sex}, {attrs.age} years")
 ```
@@ -183,8 +187,7 @@ fairface = FairFace()
 analyzer = FaceAnalyzer(
     detector,
     recognizer=recognizer,
-    age_gender=age_gender,
-    fairface=fairface,
+    attributes=[age_gender, fairface],
 )
 
 faces = analyzer.analyze(image)

docs/concepts/thresholds-calibration.md

@@ -201,17 +201,11 @@ For drawing detections, filter by confidence:
 ```python
 from uniface.draw import draw_detections
 
-# Only draw high-confidence detections
-bboxes = [f.bbox for f in faces if f.confidence > 0.7]
-scores = [f.confidence for f in faces if f.confidence > 0.7]
-landmarks = [f.landmarks for f in faces if f.confidence > 0.7]
-
+# Only draw high-confidence detections (confidence ≥ vis_threshold)
 draw_detections(
     image=image,
-    bboxes=bboxes,
-    scores=scores,
-    landmarks=landmarks,
-    vis_threshold=0.6  # Additional visualization filter
+    faces=faces,
+    vis_threshold=0.7,
 )
 ```
 

docs/contributing.md

@@ -47,6 +47,38 @@ pre-commit run --all-files
 
 ---
 
+## Commit Messages
+
+We follow [Conventional Commits](https://www.conventionalcommits.org/):
+
+```
+<type>: <short description>
+```
+
+| Type         | When to use                                      |
+|--------------|--------------------------------------------------|
+| **feat**     | New feature or capability                        |
+| **fix**      | Bug fix                                          |
+| **docs**     | Documentation changes                            |
+| **style**    | Formatting, whitespace (no logic change)         |
+| **refactor** | Code restructuring without changing behavior     |
+| **perf**     | Performance improvement                          |
+| **test**     | Adding or updating tests                         |
+| **build**    | Build system or dependencies                     |
+| **ci**       | CI/CD and pre-commit configuration               |
+| **chore**    | Routine maintenance and tooling                  |
+
+**Examples:**
+
+```
+feat: Add gaze estimation model
+fix: Correct bounding box scaling for non-square images
+ci: Add nbstripout pre-commit hook
+docs: Update installation instructions
+```
+
+---
+
 ## Pull Request Process
 
 1. Fork the repository
@@ -67,6 +99,14 @@ pre-commit run --all-files
 
 ---
 
+## Releases
+
+Releases are automated via GitHub Actions. Maintainers trigger **Actions → Release Pipeline → Run workflow** with a [PEP 440](https://peps.python.org/pep-0440/) version (e.g. `0.7.0`, `0.7.0rc1`). The pipeline runs tests, bumps `pyproject.toml` + `uniface/__init__.py`, tags the commit, publishes to PyPI, and creates a GitHub Release. Docs redeploy only for stable releases.
+
+See [CONTRIBUTING.md](https://github.com/yakhyo/uniface/blob/main/CONTRIBUTING.md#release-process) for the full process.
+
+---
+
 ## Questions?
 
 Open an issue on [GitHub](https://github.com/yakhyo/uniface/issues).

docs/datasets.md

@@ -183,6 +183,30 @@ data/
 
 ---
 
+### Head Pose Estimation
+
+#### 300W-LP
+
+Large-scale synthesized face dataset with large pose variations, generated from 300W by face profiling. Used for training head pose estimation models.
+
+| Property    | Value                         |
+| ----------- | ----------------------------- |
+| Images      | ~122,000 (synthesized)        |
+| Source      | 300W (profiled)               |
+| Pose range  | ±90° yaw                     |
+| Evaluation  | AFLW2000                      |
+| Used by     | All HeadPose models           |
+
+!!! info "Download & Reference"
+    **Paper**: [Face Alignment Across Large Poses: A 3D Solution](https://arxiv.org/abs/1511.07212)
+
+    **Training code**: [yakhyo/head-pose-estimation](https://github.com/yakhyo/head-pose-estimation)
+
+!!! note "UniFace Models"
+    All HeadPose models shipped with UniFace are trained on 300W-LP and evaluated on AFLW2000.
+
+---
+
 ### Face Parsing
 
 #### CelebAMask-HQ

docs/index.md

@@ -10,7 +10,7 @@ template: home.html
 
 # UniFace { .hero-title }
 
-<p class="hero-subtitle">All-in-One Open-Source Face Analysis Library</p>
+<p class="hero-subtitle">A Unified Face Analysis Library for Python</p>
 
 [![PyPI Version](https://img.shields.io/pypi/v/uniface.svg?label=Version)](https://pypi.org/project/uniface/)
 [![Python Version](https://img.shields.io/badge/Python-3.10%2B-blue)](https://www.python.org/)
@@ -20,7 +20,7 @@ template: home.html
 [![Kaggle Badge](https://img.shields.io/badge/Notebooks-Kaggle?label=Kaggle&color=blue)](https://www.kaggle.com/yakhyokhuja/code)
 [![Discord](https://img.shields.io/badge/Discord-Join%20Server-5865F2?logo=discord&logoColor=white)](https://discord.gg/wdzrjr7R5j)
 
-<!-- <img src="https://raw.githubusercontent.com/yakhyo/uniface/main/.github/logos/uniface_rounded_q80.webp" alt="UniFace - All-in-One Open-Source Face Analysis Library" style="max-width: 70%; margin: 1rem 0;"> -->
+<!-- <img src="https://raw.githubusercontent.com/yakhyo/uniface/main/.github/logos/uniface_rounded_q80.webp" alt="UniFace - A Unified Face Analysis Library for Python" style="max-width: 70%; margin: 1rem 0;"> -->
 
 [Get Started](quickstart.md){ .md-button .md-button--primary }
 [View on GitHub](https://github.com/yakhyo/uniface){ .md-button }
@@ -31,12 +31,12 @@ template: home.html
 
 <div class="feature-card" markdown>
 ### :material-face-recognition: Face Detection
-ONNX-optimized detectors (RetinaFace, SCRFD, YOLO) with 5-point landmarks.
+RetinaFace, SCRFD, and YOLO detectors with 5-point landmarks.
 </div>
 
 <div class="feature-card" markdown>
 ### :material-account-check: Face Recognition
-AdaFace, ArcFace, MobileFace, and SphereFace embeddings for identity verification.
+AdaFace, ArcFace, EdgeFace, MobileFace, and SphereFace embeddings for identity verification.
 </div>
 
 <div class="feature-card" markdown>
@@ -59,6 +59,11 @@ BiSeNet semantic segmentation with 19 facial component classes.
 Real-time gaze direction prediction with MobileGaze models.
 </div>
 
+<div class="feature-card" markdown>
+### :material-axis-arrow: Head Pose
+3D head orientation (pitch, yaw, roll) estimation with 6D rotation models.
+</div>
+
 <div class="feature-card" markdown>
 ### :material-motion-play: Tracking
 Multi-object tracking with BYTETracker for persistent face IDs across video frames.
@@ -85,14 +90,14 @@ FAISS-backed embedding store for fast multi-identity face search.
 
 ## Installation
 
-UniFace runs inference primarily via **ONNX Runtime**; some optional components (e.g., emotion TorchScript, torchvision NMS) require **PyTorch**.
+UniFace uses portable model runtimes for consistent inference across macOS, Linux, and Windows. Most core components run through **ONNX Runtime**, while optional components may use **PyTorch** where appropriate.
 
-**Standard**
+**CPU / Apple Silicon**
 ```bash
-pip install uniface
+pip install uniface[cpu]
 ```
 
-**GPU (CUDA)**
+**GPU (NVIDIA CUDA)**
 ```bash
 pip install uniface[gpu]
 ```
@@ -101,7 +106,7 @@ pip install uniface[gpu]
 ```bash
 git clone https://github.com/yakhyo/uniface.git
 cd uniface
-pip install -e .
+pip install -e ".[cpu]"   # or .[gpu] for CUDA
 ```
 
 ---

docs/installation.md

@@ -11,15 +11,27 @@ This guide covers all installation options for UniFace.
 
 ---
 
+## Why Two Extras?
+
+`onnxruntime` (CPU) and `onnxruntime-gpu` (CUDA) both own the same Python namespace.
+Installing both at the same time causes file conflicts and silent provider mismatches.
+UniFace exposes them as separate, mutually exclusive extras so you install exactly one.
+
+---
+
 ## Quick Install
 
-The simplest way to install UniFace:
+=== "CPU / Apple Silicon"
 
-```bash
-pip install uniface
-```
+    ```bash
+    pip install uniface[cpu]
+    ```
 
-This installs the CPU version with all core dependencies.
+=== "NVIDIA GPU (CUDA)"
+
+    ```bash
+    pip install uniface[gpu]
+    ```
 
 ---
 
@@ -27,14 +39,16 @@ This installs the CPU version with all core dependencies.
 
 ### macOS (Apple Silicon - M1/M2/M3/M4)
 
-For Apple Silicon Macs, the standard installation automatically includes ARM64 optimizations:
+The `[cpu]` extra pulls in the standard `onnxruntime` package, which has native ARM64 support
+built in since version 1.13. No additional setup is needed for CoreML acceleration.
 
 ```bash
-pip install uniface
+pip install uniface[cpu]
 ```
 
 !!! tip "Native Performance"
-    The base `onnxruntime` package has native Apple Silicon support with ARM64 optimizations built-in since version 1.13+. No additional configuration needed.
+    `onnxruntime` 1.13+ includes ARM64 optimizations out of the box.
+    UniFace automatically detects and enables `CoreMLExecutionProvider` on Apple Silicon.
 
 Verify ARM64 installation:
 
@@ -47,18 +61,22 @@ python -c "import platform; print(platform.machine())"
 
 ### Linux/Windows with NVIDIA GPU
 
-For CUDA acceleration on NVIDIA GPUs:
-
 ```bash
 pip install uniface[gpu]
 ```
 
+This installs `onnxruntime-gpu`, which includes both `CUDAExecutionProvider` and
+`CPUExecutionProvider` — no separate CPU package is needed.
+
 **Requirements:**
 
-- `uniface[gpu]` automatically installs `onnxruntime-gpu`. Requirements depend on the ORT version and execution provider.
+- NVIDIA driver compatible with your CUDA version
+- CUDA 11.x or 12.x toolkit
+- cuDNN 8.x
 
 !!! info "CUDA Compatibility"
-    See the [ONNX Runtime GPU compatibility matrix](https://onnxruntime.ai/docs/execution-providers/CUDA-ExecutionProvider.html) for matching CUDA and cuDNN versions.
+    See the [ONNX Runtime GPU compatibility matrix](https://onnxruntime.ai/docs/execution-providers/CUDA-ExecutionProvider.html)
+    for matching CUDA and cuDNN versions.
 
 Verify GPU installation:
 
@@ -70,23 +88,10 @@ print("Available providers:", ort.get_available_providers())
 
 ---
 
-### FAISS Vector Indexing
-
-For fast multi-identity face search using a FAISS index:
-
-```bash
-pip install faiss-cpu   # CPU
-pip install faiss-gpu   # NVIDIA GPU (CUDA)
-```
-
-See the [Indexing module](modules/indexing.md) for usage.
-
----
-
 ### CPU-Only (All Platforms)
 
 ```bash
-pip install uniface
+pip install uniface[cpu]
 ```
 
 Works on all platforms with automatic CPU fallback.
@@ -100,37 +105,58 @@ For development or the latest features:
 ```bash
 git clone https://github.com/yakhyo/uniface.git
 cd uniface
-pip install -e .
+
+pip install -e ".[cpu]"   # CPU / Apple Silicon
+pip install -e ".[gpu]"   # NVIDIA GPU
 ```
 
 With development dependencies:
 
 ```bash
-pip install -e ".[dev]"
+pip install -e ".[cpu,dev]"
 ```
 
 ---
 
+## FAISS Vector Store
+
+For fast multi-identity face search using a FAISS vector store:
+
+```bash
+pip install faiss-cpu   # CPU
+pip install faiss-gpu   # NVIDIA GPU (CUDA)
+```
+
+See the [Stores module](modules/stores.md) for usage.
+
+---
+
 ## Dependencies
 
-UniFace has minimal dependencies:
+UniFace has minimal core dependencies:
 
 | Package | Purpose |
 |---------|---------|
 | `numpy` | Array operations |
 | `opencv-python` | Image processing |
-| `onnxruntime` | Model inference |
 | `scikit-image` | Geometric transforms |
+| `scipy` | Signal processing |
 | `requests` | Model download |
 | `tqdm` | Progress bars |
 
-**Optional:**
+**Runtime extras (install exactly one):**
 
-| Package | Install extra | Purpose |
-|---------|---------------|---------|
-| `faiss-cpu` / `faiss-gpu` | `pip install faiss-cpu` | FAISS vector indexing |
-| `onnxruntime-gpu` | `uniface[gpu]` | CUDA acceleration |
-| `torch` | `pip install torch` | Emotion model uses TorchScript |
+| Extra | Package | Use case |
+|-------|---------|---------|
+| `uniface[cpu]` | `onnxruntime` | CPU inference, Apple Silicon |
+| `uniface[gpu]` | `onnxruntime-gpu` | NVIDIA CUDA inference |
+
+**Other optional packages:**
+
+| Package | Install | Purpose |
+|---------|---------|---------|
+| `faiss-cpu` / `faiss-gpu` | `pip install faiss-cpu` | FAISS vector store |
+| `torch` | `pip install torch` | Emotion model (TorchScript) |
 | `torchvision` | `pip install torchvision` | Faster NMS for YOLO detectors |
 
 ---
@@ -155,17 +181,81 @@ print("Installation successful!")
 
 ---
 
+## Upgrading
+
+When upgrading UniFace, stay consistent with your runtime extra:
+
+```bash
+pip install --upgrade uniface[cpu]   # or uniface[gpu]
+```
+
+If you are switching from CPU to GPU (or vice versa):
+
+```bash
+pip uninstall onnxruntime onnxruntime-gpu -y
+pip install uniface[gpu]   # install the one you want
+```
+
+---
+
+## Pre-release Versions
+
+UniFace ships release candidates and betas to PyPI ahead of stable releases (versions like `0.7.0rc1`, `0.7.0b1`, `0.7.0a1`). These let you try upcoming features before they're finalized.
+
+`pip install uniface` always installs the latest **stable** release. To opt in to pre-releases:
+
+```bash
+# Latest pre-release (if newer than latest stable)
+pip install uniface[cpu] --pre
+
+# A specific pre-release
+pip install uniface[cpu]==0.7.0rc1
+```
+
+Pre-releases are not recommended for production — APIs may still change before the stable release.
+
+---
+
 ## Troubleshooting
 
+### onnxruntime Not Found
+
+If you see:
+
+```
+ImportError: onnxruntime is not installed. Install it with one of:
+  pip install uniface[cpu]   # CPU / Apple Silicon
+  pip install uniface[gpu]   # NVIDIA GPU (CUDA)
+```
+
+You installed uniface without an extra. Run the appropriate command above.
+
+---
+
+### Both onnxruntime and onnxruntime-gpu Installed
+
+If you previously ran `pip install uniface[gpu]` on top of a `pip install uniface[cpu]`
+(or vice versa), you may have both packages installed simultaneously, which causes conflicts.
+Fix it with:
+
+```bash
+pip uninstall onnxruntime onnxruntime-gpu -y
+pip install uniface[gpu]   # or uniface[cpu]
+```
+
+---
+
 ### Import Errors
 
-If you encounter import errors, ensure you're using Python 3.10+:
+Ensure you're using Python 3.10+:
 
 ```bash
 python --version
 # Should show: Python 3.10.x or higher
 ```
 
+---
+
 ### Model Download Issues
 
 Models are automatically downloaded on first use. If downloads fail:
@@ -179,6 +269,25 @@ model_path = verify_model_weights(RetinaFaceWeights.MNET_V2)
 print(f"Model downloaded to: {model_path}")
 ```
 
+---
+
+### CUDA Not Detected
+
+1. Verify CUDA installation:
+   ```bash
+   nvidia-smi
+   ```
+
+2. Check CUDA version compatibility with ONNX Runtime.
+
+3. Reinstall the GPU extra cleanly:
+   ```bash
+   pip uninstall onnxruntime onnxruntime-gpu -y
+   pip install uniface[gpu]
+   ```
+
+---
+
 ### Performance Issues on Mac
 
 Verify you're using the ARM64 build (not x86_64 via Rosetta):

docs/license-attribution.md

@@ -20,5 +20,6 @@ UniFace is released under the [MIT License](https://opensource.org/licenses/MIT)
 | SphereFace | [yakhyo/face-recognition](https://github.com/yakhyo/face-recognition) | MIT |
 | BiSeNet | [yakhyo/face-parsing](https://github.com/yakhyo/face-parsing) | MIT |
 | MobileGaze | [yakhyo/gaze-estimation](https://github.com/yakhyo/gaze-estimation) | MIT |
+| MODNet | [yakhyo/modnet](https://github.com/yakhyo/modnet) | Apache-2.0 |
 | MiniFASNet | [yakhyo/face-anti-spoofing](https://github.com/yakhyo/face-anti-spoofing) | Apache-2.0 |
 | FairFace | [yakhyo/fairface-onnx](https://github.com/yakhyo/fairface-onnx) | CC BY 4.0 |

docs/models.md

@@ -156,6 +156,24 @@ Face recognition using angular softmax loss.
 
 ---
 
+### EdgeFace
+
+Efficient face recognition designed for edge devices, using EdgeNeXt backbone with optional LoRA compression.
+
+| Model Name      | Backbone | Params | MFLOPs | Size  | LFW    | CALFW  | CPLFW  | CFP-FP | AgeDB-30 |
+| --------------- | -------- | ------ | ------ | ----- | ------ | ------ | ------ | ------ | -------- |
+| `XXS` :material-check-circle: | EdgeNeXt | 1.24M  | 94     | ~5 MB | 99.57% | 94.83% | 90.27% | 93.63% | 94.92%   |
+| `XS_GAMMA_06`   | EdgeNeXt | 1.77M  | 154    | ~7 MB | 99.73% | 95.28% | 91.58% | 94.71% | 96.08%   |
+| `S_GAMMA_05`    | EdgeNeXt | 3.65M  | 306    | ~14 MB | 99.78% | 95.55% | 92.48% | 95.74% | 97.03%  |
+| `BASE`          | EdgeNeXt | 18.2M  | 1399   | ~70 MB | 99.83% | 96.07% | 93.75% | 97.01% | 97.60%  |
+
+!!! info "Training Data & Reference"
+    **Paper**: [EdgeFace: Efficient Face Recognition Model for Edge Devices](https://arxiv.org/abs/2307.01838v2) (IEEE T-BIOM 2024)
+
+    **Source**: [github.com/otroshi/edgeface](https://github.com/otroshi/edgeface) | [github.com/yakhyo/edgeface-onnx](https://github.com/yakhyo/edgeface-onnx)
+
+---
+
 ## Facial Landmark Models
 
 ### 106-Point Landmark Detection
@@ -257,6 +275,33 @@ Gaze direction prediction models trained on [Gaze360](datasets.md#gaze360) datas
 
 ---
 
+## Head Pose Estimation Models
+
+### HeadPose Family
+
+Head pose estimation models using 6D rotation representation. Trained on [300W-LP](datasets.md#300w-lp) dataset, evaluated on AFLW2000. Returns pitch, yaw, and roll angles in degrees.
+
+| Model Name     | Backbone | Size    | MAE*  |
+| -------------- | -------- | ------- | ----- |
+| `RESNET18` :material-check-circle: | ResNet18 | 43 MB   | 5.22° |
+| `RESNET34`   | ResNet34 | 82 MB   | 5.07° |
+| `RESNET50`   | ResNet50 | 91 MB   | 4.83° |
+| `MOBILENET_V2` | MobileNetV2 | 9.6 MB  | 5.72° |
+| `MOBILENET_V3_SMALL` | MobileNetV3-Small | 4.8 MB  | 6.31° |
+| `MOBILENET_V3_LARGE` | MobileNetV3-Large | 16 MB   | 5.58° |
+
+*MAE (Mean Absolute Error) in degrees on AFLW2000 test set — lower is better
+
+!!! info "Training Data"
+    **Dataset**: Trained on [300W-LP](datasets.md#300w-lp) (synthesized large-pose faces from 300W)
+
+    **Method**: 6D rotation representation (rotation matrix → Euler angles)
+
+!!! note "Input Requirements"
+    Requires face crop as input. Use face detection first to obtain bounding boxes.
+
+---
+
 ## Face Parsing Models
 
 ### BiSeNet Family
@@ -326,6 +371,36 @@ XSeg from DeepFaceLab outputs masks for face regions. Requires 5-point landmarks
 
 ---
 
+## Portrait Matting Models
+
+### MODNet
+
+MODNet (Real-Time Trimap-Free Portrait Matting) produces soft alpha mattes from full images without requiring a trimap. Uses MobileNetV2 backbone with low-resolution, high-resolution, and fusion branches.
+
+| Model Name | Variant | Size | Use Case |
+| ---------- | ------- | ---- | -------- |
+| `PHOTOGRAPHIC` :material-check-circle: | High-quality | 25 MB | Portrait photos |
+| `WEBCAM` | Real-time | 25 MB | Webcam feeds |
+
+!!! info "Model Details"
+    **Paper**: [MODNet: Real-Time Trimap-Free Portrait Matting via Objective Decomposition](https://arxiv.org/abs/2011.11961) (AAAI 2022)
+
+    **Source**: [yakhyo/modnet](https://github.com/yakhyo/modnet) — ported weights and clean inference codebase
+
+    **Output**: Alpha matte `(H, W)` in `[0, 1]`
+
+**Applications:**
+
+- Background removal / replacement
+- Green screen compositing
+- Video conferencing virtual backgrounds
+- Portrait editing
+
+!!! note "Input Requirements"
+    Operates on full images (not face crops). No trimap or face detection required.
+
+---
+
 ## Anti-Spoofing Models
 
 ### MiniFASNet Family
@@ -372,8 +447,10 @@ See [Model Cache & Offline Use](concepts/model-cache-offline.md) for full detail
 - **AdaFace ONNX**: [yakhyo/adaface-onnx](https://github.com/yakhyo/adaface-onnx) - ONNX export and inference
 - **Face Recognition Training**: [yakhyo/face-recognition](https://github.com/yakhyo/face-recognition) - ArcFace, MobileFace, SphereFace training code
 - **Gaze Estimation Training**: [yakhyo/gaze-estimation](https://github.com/yakhyo/gaze-estimation) - MobileGaze training code and pretrained weights
+- **Head Pose Estimation**: [yakhyo/head-pose-estimation](https://github.com/yakhyo/head-pose-estimation) - 6D rotation head pose estimation training and ONNX models
 - **Face Parsing Training**: [yakhyo/face-parsing](https://github.com/yakhyo/face-parsing) - BiSeNet training code and pretrained weights
 - **Face Segmentation**: [yakhyo/face-segmentation](https://github.com/yakhyo/face-segmentation) - XSeg ONNX Inference
+- **Portrait Matting**: [yakhyo/modnet](https://github.com/yakhyo/modnet) - MODNet ported weights and inference (from [ZHKKKe/MODNet](https://github.com/ZHKKKe/MODNet))
 - **Face Anti-Spoofing**: [yakhyo/face-anti-spoofing](https://github.com/yakhyo/face-anti-spoofing) - MiniFASNet ONNX inference (weights from [minivision-ai/Silent-Face-Anti-Spoofing](https://github.com/minivision-ai/Silent-Face-Anti-Spoofing))
 - **FairFace**: [yakhyo/fairface-onnx](https://github.com/yakhyo/fairface-onnx) - FairFace ONNX inference for race, gender, age prediction
 - **InsightFace**: [deepinsight/insightface](https://github.com/deepinsight/insightface) - Model architectures and pretrained weights
@@ -386,4 +463,5 @@ See [Model Cache & Offline Use](concepts/model-cache-offline.md) for full detail
 - **AdaFace**: [AdaFace: Quality Adaptive Margin for Face Recognition](https://arxiv.org/abs/2204.00964)
 - **ArcFace**: [Additive Angular Margin Loss for Deep Face Recognition](https://arxiv.org/abs/1801.07698)
 - **SphereFace**: [Deep Hypersphere Embedding for Face Recognition](https://arxiv.org/abs/1704.08063)
+- **MODNet**: [Real-Time Trimap-Free Portrait Matting via Objective Decomposition](https://arxiv.org/abs/2011.11961)
 - **BiSeNet**: [Bilateral Segmentation Network for Real-time Semantic Segmentation](https://arxiv.org/abs/1808.00897)

docs/modules/attributes.md

@@ -2,6 +2,11 @@
 
 Facial attribute analysis for age, gender, race, and emotion detection.
 
+<figure markdown="span">
+  ![Age & Gender Prediction](https://raw.githubusercontent.com/yakhyo/uniface/main/assets/demos/age_gender.jpg){ width="100%" }
+  <figcaption>Age and gender prediction with detection bounding boxes</figcaption>
+</figure>
+
 ---
 
 ## Available Models
@@ -30,9 +35,10 @@ age_gender = AgeGender()
 faces = detector.detect(image)
 
 for face in faces:
-    result = age_gender.predict(image, face.bbox)
+    result = age_gender.predict(image, face)
     print(f"Gender: {result.sex}")  # "Female" or "Male"
     print(f"Age: {result.age} years")
+    # face.gender and face.age are also set automatically
 ```
 
 ### Output
@@ -64,10 +70,11 @@ fairface = FairFace()
 faces = detector.detect(image)
 
 for face in faces:
-    result = fairface.predict(image, face.bbox)
+    result = fairface.predict(image, face)
     print(f"Gender: {result.sex}")
     print(f"Age Group: {result.age_group}")
     print(f"Race: {result.race}")
+    # face.gender, face.age_group, face.race are also set automatically
 ```
 
 ### Output
@@ -132,7 +139,7 @@ emotion = Emotion(model_name=DDAMFNWeights.AFFECNET7)
 faces = detector.detect(image)
 
 for face in faces:
-    result = emotion.predict(image, face.landmarks)
+    result = emotion.predict(image, face)
     print(f"Emotion: {result.emotion}")
     print(f"Confidence: {result.confidence:.2%}")
 ```
@@ -179,6 +186,22 @@ emotion = Emotion(model_name=DDAMFNWeights.AFFECNET8)
 
 ---
 
+## Factory Function
+
+Use `create_attribute_predictor()` for dynamic model selection:
+
+```python
+from uniface import create_attribute_predictor
+
+age_gender = create_attribute_predictor('age_gender')
+fairface = create_attribute_predictor('fairface')
+emotion = create_attribute_predictor('emotion')
+```
+
+Available model names: `'age_gender'`, `'fairface'`, `'emotion'`.
+
+---
+
 ## Combining Models
 
 ### Full Attribute Analysis
@@ -195,10 +218,10 @@ faces = detector.detect(image)
 
 for face in faces:
     # Get exact age from AgeGender
-    ag_result = age_gender.predict(image, face.bbox)
+    ag_result = age_gender.predict(image, face)
 
     # Get race from FairFace
-    ff_result = fairface.predict(image, face.bbox)
+    ff_result = fairface.predict(image, face)
 
     print(f"Gender: {ag_result.sex}")
     print(f"Exact Age: {ag_result.age}")
@@ -215,7 +238,7 @@ from uniface.detection import RetinaFace
 
 analyzer = FaceAnalyzer(
     RetinaFace(),
-    age_gender=AgeGender(),
+    attributes=[AgeGender()],
 )
 
 faces = analyzer.analyze(image)
@@ -257,7 +280,7 @@ def draw_attributes(image, face, result):
 
 # Usage
 for face in faces:
-    result = age_gender.predict(image, face.bbox)
+    result = age_gender.predict(image, face)
     image = draw_attributes(image, face, result)
 
 cv2.imwrite("attributes.jpg", image)

docs/modules/detection.md

@@ -2,6 +2,11 @@
 
 Face detection is the first step in any face analysis pipeline. UniFace provides four detection models.
 
+<figure markdown="span">
+  ![Face Detection](https://raw.githubusercontent.com/yakhyo/uniface/main/assets/demos/detection.jpg){ width="100%" }
+  <figcaption>SCRFD detection with corner-style bounding boxes and 5-point landmarks</figcaption>
+</figure>
+
 ---
 
 ## Available Models
@@ -264,10 +269,8 @@ from uniface.draw import draw_detections
 
 draw_detections(
     image=image,
-    bboxes=[f.bbox for f in faces],
-    scores=[f.confidence for f in faces],
-    landmarks=[f.landmarks for f in faces],
-    vis_threshold=0.6
+    faces=faces,
+    vis_threshold=0.6,
 )
 
 cv2.imwrite("result.jpg", image)

docs/modules/gaze.md

@@ -2,6 +2,11 @@
 
 Gaze estimation predicts where a person is looking (pitch and yaw angles).
 
+<figure markdown="span">
+  ![Gaze Estimation](https://raw.githubusercontent.com/yakhyo/uniface/main/assets/demos/gaze.jpg){ width="100%" }
+  <figcaption>Gaze direction arrows with pitch/yaw angle labels</figcaption>
+</figure>
+
 ---
 
 ## Available Models
@@ -267,6 +272,7 @@ gaze = create_gaze_estimator()  # Returns MobileGaze
 
 ## Next Steps
 
+- [Head Pose Estimation](headpose.md) - 3D head orientation
 - [Anti-Spoofing](spoofing.md) - Face liveness detection
 - [Privacy](privacy.md) - Face anonymization
 - [Video Recipe](../recipes/video-webcam.md) - Real-time processing

docs/modules/headpose.md

@@ -0,0 +1,237 @@
+# Head Pose Estimation
+
+Head pose estimation predicts the 3D orientation of a person's head (pitch, yaw, and roll angles).
+
+<figure markdown="span">
+  ![Head Pose Estimation](https://raw.githubusercontent.com/yakhyo/uniface/main/assets/demos/headpose.jpg){ width="100%" }
+  <figcaption>3D head pose visualization with pitch, yaw, and roll angles</figcaption>
+</figure>
+
+---
+
+## Available Models
+
+| Model | Backbone | Size | MAE* |
+|-------|----------|------|------|
+| **ResNet18** :material-check-circle: | ResNet18 | 43 MB | 5.22° |
+| ResNet34 | ResNet34 | 82 MB | 5.07° |
+| ResNet50 | ResNet50 | 91 MB | 4.83° |
+| MobileNetV2 | MobileNetV2 | 9.6 MB | 5.72° |
+| MobileNetV3-Small | MobileNetV3 | 4.8 MB | 6.31° |
+| MobileNetV3-Large | MobileNetV3 | 16 MB | 5.58° |
+
+*MAE = Mean Absolute Error on AFLW2000 test set (lower is better)
+
+---
+
+## Basic Usage
+
+```python
+import cv2
+from uniface.detection import RetinaFace
+from uniface.headpose import HeadPose
+
+detector = RetinaFace()
+head_pose = HeadPose()
+
+image = cv2.imread("photo.jpg")
+faces = detector.detect(image)
+
+for face in faces:
+    # Crop face
+    x1, y1, x2, y2 = map(int, face.bbox)
+    face_crop = image[y1:y2, x1:x2]
+
+    if face_crop.size > 0:
+        # Estimate head pose
+        result = head_pose.estimate(face_crop)
+        print(f"Pitch: {result.pitch:.1f}°, Yaw: {result.yaw:.1f}°, Roll: {result.roll:.1f}°")
+```
+
+---
+
+## Model Variants
+
+```python
+from uniface.headpose import HeadPose
+from uniface.constants import HeadPoseWeights
+
+# Default (ResNet18, recommended balance of speed and accuracy)
+hp = HeadPose()
+
+# Lightweight for mobile/edge
+hp = HeadPose(model_name=HeadPoseWeights.MOBILENET_V3_SMALL)
+
+# Higher accuracy
+hp = HeadPose(model_name=HeadPoseWeights.RESNET50)
+```
+
+---
+
+## Output Format
+
+```python
+result = head_pose.estimate(face_crop)
+
+# HeadPoseResult dataclass
+result.pitch  # Rotation around X-axis in degrees
+result.yaw    # Rotation around Y-axis in degrees
+result.roll   # Rotation around Z-axis in degrees
+```
+
+### Angle Convention
+
+```
+          pitch > 0 (looking down)
+               │
+               │
+yaw < 0  ─────┼───── yaw > 0
+(looking left) │     (looking right)
+               │
+          pitch < 0 (looking up)
+
+roll > 0 = clockwise tilt
+roll < 0 = counter-clockwise tilt
+```
+
+- **Pitch**: Rotation around X-axis (positive = looking down)
+- **Yaw**: Rotation around Y-axis (positive = looking right)
+- **Roll**: Rotation around Z-axis (positive = tilting clockwise)
+
+---
+
+## Visualization
+
+### 3D Cube (default)
+
+The default visualization draws a wireframe cube oriented to match the head pose.
+
+```python
+from uniface.draw import draw_head_pose
+
+faces = detector.detect(image)
+
+for face in faces:
+    x1, y1, x2, y2 = map(int, face.bbox)
+    face_crop = image[y1:y2, x1:x2]
+
+    if face_crop.size > 0:
+        result = head_pose.estimate(face_crop)
+
+        # Draw cube on image (default)
+        draw_head_pose(image, face.bbox, result.pitch, result.yaw, result.roll)
+
+cv2.imwrite("headpose_output.jpg", image)
+```
+
+### Axis Visualization
+
+```python
+from uniface.draw import draw_head_pose
+
+# X/Y/Z coordinate axes
+draw_head_pose(image, face.bbox, result.pitch, result.yaw, result.roll, draw_type='axis')
+```
+
+### Low-Level Drawing Functions
+
+```python
+from uniface.draw import draw_head_pose_cube, draw_head_pose_axis
+
+# Draw cube directly
+draw_head_pose_cube(image, yaw=10.0, pitch=-5.0, roll=2.0, bbox=[100, 100, 250, 280])
+
+# Draw axes directly
+draw_head_pose_axis(image, yaw=10.0, pitch=-5.0, roll=2.0, bbox=[100, 100, 250, 280])
+```
+
+---
+
+## Real-Time Head Pose Tracking
+
+```python
+import cv2
+from uniface.detection import RetinaFace
+from uniface.headpose import HeadPose
+from uniface.draw import draw_head_pose
+
+detector = RetinaFace()
+head_pose = HeadPose()
+
+cap = cv2.VideoCapture(0)
+
+while True:
+    ret, frame = cap.read()
+    if not ret:
+        break
+
+    faces = detector.detect(frame)
+
+    for face in faces:
+        x1, y1, x2, y2 = map(int, face.bbox)
+        face_crop = frame[y1:y2, x1:x2]
+
+        if face_crop.size > 0:
+            result = head_pose.estimate(face_crop)
+            draw_head_pose(frame, face.bbox, result.pitch, result.yaw, result.roll)
+
+    cv2.imshow("Head Pose Estimation", frame)
+
+    if cv2.waitKey(1) & 0xFF == ord('q'):
+        break
+
+cap.release()
+cv2.destroyAllWindows()
+```
+
+---
+
+## Use Cases
+
+### Driver Drowsiness Detection
+
+```python
+def is_head_drooping(result, pitch_threshold=-15):
+    """Check if the head is drooping (looking down significantly)."""
+    return result.pitch < pitch_threshold
+
+result = head_pose.estimate(face_crop)
+if is_head_drooping(result):
+    print("Warning: Head drooping detected")
+```
+
+### Attention Monitoring
+
+```python
+def is_facing_forward(result, threshold=20):
+    """Check if the person is facing roughly forward."""
+    return (
+        abs(result.pitch) < threshold
+        and abs(result.yaw) < threshold
+        and abs(result.roll) < threshold
+    )
+
+result = head_pose.estimate(face_crop)
+if is_facing_forward(result):
+    print("Facing forward")
+else:
+    print("Looking away")
+```
+
+---
+
+## Factory Function
+
+```python
+from uniface.headpose import create_head_pose_estimator
+
+hp = create_head_pose_estimator()  # Returns HeadPose
+```
+
+---
+
+## Next Steps
+
+- [Gaze Estimation](gaze.md) - Eye gaze direction
+- [Anti-Spoofing](spoofing.md) - Face liveness detection
+- [Video Recipe](../recipes/video-webcam.md) - Real-time processing

docs/modules/landmarks.md

@@ -2,6 +2,11 @@
 
 Facial landmark detection provides precise localization of facial features.
 
+<figure markdown="span">
+  ![106-Point Landmarks](https://raw.githubusercontent.com/yakhyo/uniface/main/assets/demos/landmarks.jpg){ width="50%" }
+  <figcaption>106-point facial landmark localization</figcaption>
+</figure>
+
 ---
 
 ## Available Models

docs/modules/matting.md

@@ -0,0 +1,157 @@
+# Portrait Matting
+
+Portrait matting produces a soft alpha matte separating the foreground (person) from the background — no trimap needed.
+
+<figure markdown="span">
+  ![Portrait Matting](https://raw.githubusercontent.com/yakhyo/uniface/main/assets/demos/matting.jpg){ width="100%" }
+  <figcaption>MODNet: Input → Matte → Green Screen</figcaption>
+</figure>
+
+---
+
+## Available Models
+
+| Model | Variant | Size | Use Case |
+|-------|---------|------|----------|
+| **MODNet Photographic** :material-check-circle: | PHOTOGRAPHIC | 25 MB | High-quality portrait photos |
+| MODNet Webcam | WEBCAM | 25 MB | Real-time webcam feeds |
+
+---
+
+## Basic Usage
+
+```python
+import cv2
+from uniface.matting import MODNet
+
+matting = MODNet()
+
+image = cv2.imread("photo.jpg")
+matte = matting.predict(image)
+
+print(f"Matte shape: {matte.shape}")   # (H, W)
+print(f"Matte dtype: {matte.dtype}")   # float32
+print(f"Matte range: [{matte.min():.2f}, {matte.max():.2f}]")  # [0, 1]
+```
+
+---
+
+## Model Variants
+
+```python
+from uniface.matting import MODNet
+from uniface.constants import MODNetWeights
+
+# Photographic (default) — best for photos
+matting = MODNet()
+
+# Webcam — optimized for real-time
+matting = MODNet(model_name=MODNetWeights.WEBCAM)
+
+# Custom input size
+matting = MODNet(input_size=256)
+```
+
+| Parameter | Default | Description |
+|-----------|---------|-------------|
+| `model_name` | `PHOTOGRAPHIC` | Model variant to load |
+| `input_size` | `512` | Target shorter-side size for preprocessing |
+| `providers` | `None` | ONNX Runtime execution providers |
+
+---
+
+## Applications
+
+### Transparent Background (RGBA)
+
+```python
+import cv2
+import numpy as np
+
+matting = MODNet()
+image = cv2.imread("photo.jpg")
+matte = matting.predict(image)
+
+rgba = cv2.cvtColor(image, cv2.COLOR_BGR2BGRA)
+rgba[:, :, 3] = (matte * 255).astype(np.uint8)
+cv2.imwrite("transparent.png", rgba)
+```
+
+### Green Screen
+
+```python
+import numpy as np
+
+matte_3ch = matte[:, :, np.newaxis]
+bg = np.full_like(image, (0, 177, 64), dtype=np.uint8)
+green = (image * matte_3ch + bg * (1 - matte_3ch)).astype(np.uint8)
+cv2.imwrite("green_screen.jpg", green)
+```
+
+### Custom Background
+
+```python
+import cv2
+import numpy as np
+
+background = cv2.imread("beach.jpg")
+background = cv2.resize(background, (image.shape[1], image.shape[0]))
+
+matte_3ch = matte[:, :, np.newaxis]
+result = (image * matte_3ch + background * (1 - matte_3ch)).astype(np.uint8)
+cv2.imwrite("custom_bg.jpg", result)
+```
+
+### Webcam Matting
+
+```python
+import cv2
+import numpy as np
+from uniface.matting import MODNet
+
+matting = MODNet(model_name="modnet_webcam")
+cap = cv2.VideoCapture(0)
+
+while True:
+    ret, frame = cap.read()
+    if not ret:
+        break
+
+    matte = matting.predict(frame)
+    matte_3ch = matte[:, :, np.newaxis]
+    bg = np.full_like(frame, (0, 177, 64), dtype=np.uint8)
+    result = (frame * matte_3ch + bg * (1 - matte_3ch)).astype(np.uint8)
+
+    cv2.imshow("Matting", np.hstack([frame, result]))
+    if cv2.waitKey(1) & 0xFF == ord("q"):
+        break
+
+cap.release()
+cv2.destroyAllWindows()
+```
+
+---
+
+## Factory Function
+
+```python
+from uniface.matting import create_matting_model
+from uniface.constants import MODNetWeights
+
+# Default (Photographic)
+matting = create_matting_model()
+
+# With enum
+matting = create_matting_model(MODNetWeights.WEBCAM)
+
+# With string
+matting = create_matting_model("modnet_webcam")
+```
+
+---
+
+## Next Steps
+
+- [Parsing](parsing.md) - Face semantic segmentation
+- [Privacy](privacy.md) - Face anonymization
+- [Detection](detection.md) - Face detection

docs/modules/parsing.md

@@ -2,6 +2,16 @@
 
 Face parsing segments faces into semantic components or face regions.
 
+<figure markdown="span">
+  ![Face Parsing](https://raw.githubusercontent.com/yakhyo/uniface/main/assets/demos/parsing.jpg){ width="80%" }
+  <figcaption>BiSeNet face parsing with 19 semantic component classes</figcaption>
+</figure>
+
+<figure markdown="span">
+  ![Face Segmentation](https://raw.githubusercontent.com/yakhyo/uniface/main/assets/demos/segmentation.jpg){ width="80%" }
+  <figcaption>XSeg face region segmentation mask</figcaption>
+</figure>
+
 ---
 
 ## Available Models

docs/modules/privacy.md

@@ -2,6 +2,11 @@
 
 Face anonymization protects privacy by blurring or obscuring faces in images and videos.
 
+<figure markdown="span">
+  ![Face Anonymization](https://raw.githubusercontent.com/yakhyo/uniface/main/assets/demos/anonymization.jpg){ width="100%" }
+  <figcaption>Five anonymization methods: pixelate, gaussian, blackout, elliptical, and median</figcaption>
+</figure>
+
 ---
 
 ## Available Methods

docs/modules/recognition.md

@@ -2,6 +2,11 @@
 
 Face recognition extracts embeddings for identity verification and face search.
 
+<figure markdown="span">
+  ![Face Verification](https://raw.githubusercontent.com/yakhyo/uniface/main/assets/demos/verification.jpg){ width="80%" }
+  <figcaption>Pairwise face verification with cosine similarity scores</figcaption>
+</figure>
+
 ---
 
 ## Available Models
@@ -10,6 +15,7 @@ Face recognition extracts embeddings for identity verification and face search.
 |-------|----------|------|---------------|
 | **AdaFace** | IR-18/IR-101 | 92-249 MB | 512 |
 | **ArcFace** | MobileNet/ResNet | 8-166 MB | 512 |
+| **EdgeFace** | EdgeNeXt/LoRA | 5-70 MB | 512 |
 | **MobileFace** | MobileNet V2/V3 | 1-10 MB | 512 |
 | **SphereFace** | Sphere20/36 | 50-92 MB | 512 |
 
@@ -113,6 +119,64 @@ recognizer = ArcFace(providers=['CPUExecutionProvider'])
 
 ---
 
+## EdgeFace
+
+Efficient face recognition designed for edge devices, using an EdgeNeXt backbone with optional LoRA low-rank compression. Competition-winning entry (compact track) at EFaR 2023, IJCB.
+
+### Basic Usage
+
+```python
+from uniface.detection import RetinaFace
+from uniface.recognition import EdgeFace
+
+detector = RetinaFace()
+recognizer = EdgeFace()
+
+# Detect face
+faces = detector.detect(image)
+
+# Extract embedding
+if faces:
+    embedding = recognizer.get_normalized_embedding(image, faces[0].landmarks)
+    print(f"Embedding shape: {embedding.shape}")  # (512,)
+```
+
+### Model Variants
+
+```python
+from uniface.recognition import EdgeFace
+from uniface.constants import EdgeFaceWeights
+
+# Ultra-compact (default)
+recognizer = EdgeFace(model_name=EdgeFaceWeights.XXS)
+
+# Compact with LoRA
+recognizer = EdgeFace(model_name=EdgeFaceWeights.XS_GAMMA_06)
+
+# Small with LoRA
+recognizer = EdgeFace(model_name=EdgeFaceWeights.S_GAMMA_05)
+
+# Full-size
+recognizer = EdgeFace(model_name=EdgeFaceWeights.BASE)
+
+# Force CPU execution
+recognizer = EdgeFace(providers=['CPUExecutionProvider'])
+```
+
+| Variant | Params | MFLOPs | Size | LFW | CALFW | CPLFW | CFP-FP | AgeDB-30 |
+|---------|--------|--------|------|-----|-------|-------|--------|----------|
+| **XXS** :material-check-circle: | 1.24M | 94 | ~5 MB | 99.57% | 94.83% | 90.27% | 93.63% | 94.92% |
+| XS_GAMMA_06 | 1.77M | 154 | ~7 MB | 99.73% | 95.28% | 91.58% | 94.71% | 96.08% |
+| S_GAMMA_05 | 3.65M | 306 | ~14 MB | 99.78% | 95.55% | 92.48% | 95.74% | 97.03% |
+| BASE | 18.2M | 1399 | ~70 MB | 99.83% | 96.07% | 93.75% | 97.01% | 97.60% |
+
+!!! info "Reference"
+    **Paper**: [EdgeFace: Efficient Face Recognition Model for Edge Devices](https://arxiv.org/abs/2307.01838v2) (IEEE T-BIOM 2024)
+
+    **Source**: [github.com/otroshi/edgeface](https://github.com/otroshi/edgeface)
+
+---
+
 ## MobileFace
 
 Lightweight face recognition models with MobileNet backbones.
@@ -287,9 +351,10 @@ else:
 ```python
 from uniface.recognition import create_recognizer
 
-# Available methods: 'arcface', 'adaface', 'mobileface', 'sphereface'
+# Available methods: 'arcface', 'adaface', 'edgeface', 'mobileface', 'sphereface'
 recognizer = create_recognizer('arcface')
 recognizer = create_recognizer('adaface')
+recognizer = create_recognizer('edgeface')
 ```
 
 ---

docs/modules/stores.md

@@ -1,4 +1,4 @@
-# Indexing
+# Stores
 
 FAISS-backed vector store for fast similarity search over embeddings.
 
@@ -12,7 +12,7 @@ FAISS-backed vector store for fast similarity search over embeddings.
 ## FAISS
 
 ```python
-from uniface.indexing import FAISS
+from uniface.stores import FAISS
 ```
 
 A thin wrapper around a FAISS `IndexFlatIP` (inner-product) index. Vectors
@@ -134,7 +134,7 @@ loaded = store.load()  # True if files exist
 import cv2
 from uniface.detection import RetinaFace
 from uniface.recognition import ArcFace
-from uniface.indexing import FAISS
+from uniface.stores import FAISS
 
 detector = RetinaFace()
 recognizer = ArcFace()

docs/notebooks.md

@@ -12,12 +12,15 @@ Run UniFace examples directly in your browser with Google Colab, or download and
 | [Face Alignment](https://github.com/yakhyo/uniface/blob/main/examples/02_face_alignment.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/yakhyo/uniface/blob/main/examples/02_face_alignment.ipynb) | Align faces for recognition |
 | [Face Verification](https://github.com/yakhyo/uniface/blob/main/examples/03_face_verification.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/yakhyo/uniface/blob/main/examples/03_face_verification.ipynb) | Compare faces for identity |
 | [Face Search](https://github.com/yakhyo/uniface/blob/main/examples/04_face_search.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/yakhyo/uniface/blob/main/examples/04_face_search.ipynb) | Find a person in group photos |
-| [Face Analyzer](https://github.com/yakhyo/uniface/blob/main/examples/05_face_analyzer.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/yakhyo/uniface/blob/main/examples/05_face_analyzer.ipynb) | All-in-one face analysis |
+| [Face Analyzer](https://github.com/yakhyo/uniface/blob/main/examples/05_face_analyzer.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/yakhyo/uniface/blob/main/examples/05_face_analyzer.ipynb) | Unified face analysis |
 | [Face Parsing](https://github.com/yakhyo/uniface/blob/main/examples/06_face_parsing.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/yakhyo/uniface/blob/main/examples/06_face_parsing.ipynb) | Semantic face segmentation |
 | [Face Anonymization](https://github.com/yakhyo/uniface/blob/main/examples/07_face_anonymization.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/yakhyo/uniface/blob/main/examples/07_face_anonymization.ipynb) | Privacy-preserving blur |
 | [Gaze Estimation](https://github.com/yakhyo/uniface/blob/main/examples/08_gaze_estimation.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/yakhyo/uniface/blob/main/examples/08_gaze_estimation.ipynb) | Gaze direction estimation |
 | [Face Segmentation](https://github.com/yakhyo/uniface/blob/main/examples/09_face_segmentation.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/yakhyo/uniface/blob/main/examples/09_face_segmentation.ipynb) | Face segmentation with XSeg |
 | [Face Vector Store](https://github.com/yakhyo/uniface/blob/main/examples/10_face_vector_store.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/yakhyo/uniface/blob/main/examples/10_face_vector_store.ipynb) | FAISS-backed face database |
+| [Head Pose Estimation](https://github.com/yakhyo/uniface/blob/main/examples/11_head_pose_estimation.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/yakhyo/uniface/blob/main/examples/11_head_pose_estimation.ipynb) | 3D head orientation estimation |
+| [Face Recognition](https://github.com/yakhyo/uniface/blob/main/examples/12_face_recognition.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/yakhyo/uniface/blob/main/examples/12_face_recognition.ipynb) | Standalone face recognition pipeline |
+| [Portrait Matting](https://github.com/yakhyo/uniface/blob/main/examples/13_portrait_matting.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/yakhyo/uniface/blob/main/examples/13_portrait_matting.ipynb) | Portrait matting with MODNet |
 
 ---
 
@@ -31,7 +34,7 @@ git clone https://github.com/yakhyo/uniface.git
 cd uniface
 
 # Install dependencies
-pip install uniface jupyter
+pip install "uniface[cpu]" jupyter   # or uniface[gpu] for CUDA
 
 # Launch Jupyter
 jupyter notebook examples/

docs/quickstart.md

@@ -54,19 +54,8 @@ detector = RetinaFace()
 image = cv2.imread("photo.jpg")
 faces = detector.detect(image)
 
-# Extract visualization data
-bboxes = [f.bbox for f in faces]
-scores = [f.confidence for f in faces]
-landmarks = [f.landmarks for f in faces]
-
 # Draw on image
-draw_detections(
-    image=image,
-    bboxes=bboxes,
-    scores=scores,
-    landmarks=landmarks,
-    vis_threshold=0.6,
-)
+draw_detections(image=image, faces=faces, vis_threshold=0.6)
 
 # Save result
 cv2.imwrite("output.jpg", image)
@@ -80,7 +69,6 @@ Compare two faces:
 
 ```python
 import cv2
-import numpy as np
 from uniface.detection import RetinaFace
 from uniface.recognition import ArcFace
 
@@ -97,12 +85,13 @@ faces1 = detector.detect(image1)
 faces2 = detector.detect(image2)
 
 if faces1 and faces2:
-    # Extract embeddings
+    # Extract embeddings (normalized 1-D vectors)
     emb1 = recognizer.get_normalized_embedding(image1, faces1[0].landmarks)
     emb2 = recognizer.get_normalized_embedding(image2, faces2[0].landmarks)
 
-    # Compute similarity (cosine similarity)
-    similarity = np.dot(emb1, emb2.T)[0][0]
+    # Compute cosine similarity
+    from uniface import compute_similarity
+    similarity = compute_similarity(emb1, emb2, normalized=True)
 
     # Interpret result
     if similarity > 0.6:
@@ -135,7 +124,7 @@ faces = detector.detect(image)
 
 # Predict attributes
 for i, face in enumerate(faces):
-    result = age_gender.predict(image, face.bbox)
+    result = age_gender.predict(image, face)
     print(f"Face {i+1}: {result.sex}, {result.age} years old")
 ```
 
@@ -164,7 +153,7 @@ image = cv2.imread("photo.jpg")
 faces = detector.detect(image)
 
 for i, face in enumerate(faces):
-    result = fairface.predict(image, face.bbox)
+    result = fairface.predict(image, face)
     print(f"Face {i+1}: {result.sex}, {result.age_group}, {result.race}")
 ```
 
@@ -234,6 +223,36 @@ cv2.imwrite("gaze_output.jpg", image)
 
 ---
 
+## Head Pose Estimation
+
+```python
+import cv2
+from uniface.detection import RetinaFace
+from uniface.headpose import HeadPose
+from uniface.draw import draw_head_pose
+
+detector = RetinaFace()
+head_pose = HeadPose()
+
+image = cv2.imread("photo.jpg")
+faces = detector.detect(image)
+
+for i, face in enumerate(faces):
+    x1, y1, x2, y2 = map(int, face.bbox[:4])
+    face_crop = image[y1:y2, x1:x2]
+
+    if face_crop.size > 0:
+        result = head_pose.estimate(face_crop)
+        print(f"Face {i+1}: pitch={result.pitch:.1f}°, yaw={result.yaw:.1f}°, roll={result.roll:.1f}°")
+
+        # Draw 3D cube visualization
+        draw_head_pose(image, face.bbox, result.pitch, result.yaw, result.roll)
+
+cv2.imwrite("headpose_output.jpg", image)
+```
+
+---
+
 ## Face Parsing
 
 Segment face into semantic components:
@@ -261,6 +280,34 @@ print(f"Detected {len(np.unique(mask))} facial components")
 
 ---
 
+## Portrait Matting
+
+Remove backgrounds without a trimap:
+
+```python
+import cv2
+import numpy as np
+from uniface.matting import MODNet
+
+matting = MODNet()
+
+image = cv2.imread("portrait.jpg")
+matte = matting.predict(image)  # (H, W) float32 in [0, 1]
+
+# Transparent PNG
+rgba = cv2.cvtColor(image, cv2.COLOR_BGR2BGRA)
+rgba[:, :, 3] = (matte * 255).astype(np.uint8)
+cv2.imwrite("transparent.png", rgba)
+
+# Green screen
+matte_3ch = matte[:, :, np.newaxis]
+bg = np.full_like(image, (0, 177, 64), dtype=np.uint8)
+result = (image * matte_3ch + bg * (1 - matte_3ch)).astype(np.uint8)
+cv2.imwrite("green_screen.jpg", result)
+```
+
+---
+
 ## Face Anonymization
 
 Blur faces for privacy protection:
@@ -342,10 +389,7 @@ while True:
 
     faces = detector.detect(frame)
 
-    bboxes = [f.bbox for f in faces]
-    scores = [f.confidence for f in faces]
-    landmarks = [f.landmarks for f in faces]
-    draw_detections(image=frame, bboxes=bboxes, scores=scores, landmarks=landmarks)
+    draw_detections(image=frame, faces=faces)
 
     cv2.imshow("UniFace - Press 'q' to quit", frame)
 
@@ -424,6 +468,7 @@ For detailed model comparisons and benchmarks, see the [Model Zoo](models.md).
 | Recognition | `ArcFace`, `AdaFace`, `MobileFace`, `SphereFace` |
 | Tracking | `BYTETracker` |
 | Gaze | `MobileGaze` (ResNet18/34/50, MobileNetV2, MobileOneS0) |
+| Head Pose | `HeadPose` (ResNet18/34/50, MobileNetV2/V3) |
 | Parsing | `BiSeNet` (ResNet18/34) |
 | Attributes | `AgeGender`, `FairFace`, `Emotion` |
 | Anti-Spoofing | `MiniFASNet` (V1SE, V2) |
@@ -470,12 +515,13 @@ from uniface.recognition import ArcFace, AdaFace
 from uniface.attribute import AgeGender, FairFace
 from uniface.landmark import Landmark106
 from uniface.gaze import MobileGaze
+from uniface.headpose import HeadPose
 from uniface.parsing import BiSeNet, XSeg
 from uniface.privacy import BlurFace
 from uniface.spoofing import MiniFASNet
 from uniface.tracking import BYTETracker
 from uniface.analyzer import FaceAnalyzer
-from uniface.indexing import FAISS  # pip install faiss-cpu
+from uniface.stores import FAISS  # pip install faiss-cpu
 from uniface.draw import draw_detections, draw_tracks
 ```
 

docs/recipes/face-search.md

@@ -52,7 +52,7 @@ python tools/search.py --reference ref.jpg --source 0  # webcam
 ## Vector Search (FAISS index)
 
 For identifying faces against a database of many known people, use the
-[`FAISS`](../modules/indexing.md) vector store.
+[`FAISS`](../modules/stores.md) vector store.
 
 !!! info "Install extra"
 `bash
@@ -80,7 +80,7 @@ import cv2
 from pathlib import Path
 from uniface.detection import RetinaFace
 from uniface.recognition import ArcFace
-from uniface.indexing import FAISS
+from uniface.stores import FAISS
 
 detector = RetinaFace()
 recognizer = ArcFace()
@@ -112,7 +112,7 @@ python tools/faiss_search.py build --faces-dir dataset/ --db-path ./my_index
 import cv2
 from uniface.detection import RetinaFace
 from uniface.recognition import ArcFace
-from uniface.indexing import FAISS
+from uniface.stores import FAISS
 
 detector = RetinaFace()
 recognizer = ArcFace()
@@ -143,7 +143,7 @@ python tools/faiss_search.py run --db-path ./my_index --source 0  # webcam
 ### Manage the index
 
 ```python
-from uniface.indexing import FAISS
+from uniface.stores import FAISS
 
 store = FAISS(db_path="./my_index")
 store.load()
@@ -160,7 +160,7 @@ store.save()
 
 ## See Also
 
-- [Indexing Module](../modules/indexing.md) - Full `FAISS` API reference
+- [Stores Module](../modules/stores.md) - Full `FAISS` API reference
 - [Recognition Module](../modules/recognition.md) - Face recognition details
 - [Video & Webcam](video-webcam.md) - Real-time processing
 - [Concepts: Thresholds](../concepts/thresholds-calibration.md) - Tuning similarity thresholds

docs/recipes/image-pipeline.md

@@ -34,7 +34,7 @@ def process_image(image_path):
         embedding = recognizer.get_normalized_embedding(image, face.landmarks)
 
         # Step 3: Predict attributes
-        attrs = age_gender.predict(image, face.bbox)
+        attrs = age_gender.predict(image, face)
 
         results.append({
             'face_id': i,
@@ -48,12 +48,7 @@ def process_image(image_path):
         print(f"  Face {i+1}: {attrs.sex}, {attrs.age} years old")
 
     # Visualize
-    draw_detections(
-        image=image,
-        bboxes=[f.bbox for f in faces],
-        scores=[f.confidence for f in faces],
-        landmarks=[f.landmarks for f in faces]
-    )
+    draw_detections(image=image, faces=faces)
 
     return image, results
 
@@ -83,7 +78,7 @@ age_gender = AgeGender()
 analyzer = FaceAnalyzer(
     detector,
     recognizer=recognizer,
-    age_gender=age_gender,
+    attributes=[age_gender],
 )
 
 # Process image
@@ -109,11 +104,12 @@ import numpy as np
 from uniface.attribute import AgeGender, FairFace
 from uniface.detection import RetinaFace
 from uniface.gaze import MobileGaze
+from uniface.headpose import HeadPose
 from uniface.landmark import Landmark106
 from uniface.recognition import ArcFace
 from uniface.parsing import BiSeNet
 from uniface.spoofing import MiniFASNet
-from uniface.draw import draw_detections, draw_gaze
+from uniface.draw import draw_detections, draw_gaze, draw_head_pose
 
 class FaceAnalysisPipeline:
     def __init__(self):
@@ -124,6 +120,7 @@ class FaceAnalysisPipeline:
         self.fairface = FairFace()
         self.landmarker = Landmark106()
         self.gaze = MobileGaze()
+        self.head_pose = HeadPose()
         self.parser = BiSeNet()
         self.spoofer = MiniFASNet()
 
@@ -145,12 +142,12 @@ class FaceAnalysisPipeline:
             )
 
             # Attributes
-            ag_result = self.age_gender.predict(image, face.bbox)
+            ag_result = self.age_gender.predict(image, face)
             result['age'] = ag_result.age
             result['gender'] = ag_result.sex
 
             # FairFace attributes
-            ff_result = self.fairface.predict(image, face.bbox)
+            ff_result = self.fairface.predict(image, face)
             result['age_group'] = ff_result.age_group
             result['race'] = ff_result.race
 
@@ -167,6 +164,13 @@ class FaceAnalysisPipeline:
                 result['gaze_pitch'] = gaze_result.pitch
                 result['gaze_yaw'] = gaze_result.yaw
 
+            # Head pose estimation
+            if face_crop.size > 0:
+                hp_result = self.head_pose.estimate(face_crop)
+                result['head_pitch'] = hp_result.pitch
+                result['head_yaw'] = hp_result.yaw
+                result['head_roll'] = hp_result.roll
+
             # Face parsing
             if face_crop.size > 0:
                 result['parsing_mask'] = self.parser.parse(face_crop)
@@ -189,6 +193,7 @@ for i, r in enumerate(results):
     print(f"  Gender: {r['gender']}, Age: {r['age']}")
     print(f"  Race: {r['race']}, Age Group: {r['age_group']}")
     print(f"  Gaze: pitch={np.degrees(r['gaze_pitch']):.1f}°")
+    print(f"  Head Pose: P={r['head_pitch']:.1f}° Y={r['head_yaw']:.1f}° R={r['head_roll']:.1f}°")
     print(f"  Real: {r['is_real']} ({r['spoof_confidence']:.1%})")
 ```
 
@@ -220,7 +225,7 @@ def visualize_analysis(image_path, output_path):
         cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), 2)
 
         # Age and gender
-        attrs = age_gender.predict(image, face.bbox)
+        attrs = age_gender.predict(image, face)
         label = f"{attrs.sex}, {attrs.age}y"
         cv2.putText(image, label, (x1, y1 - 10),
                     cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)
@@ -268,6 +273,11 @@ def results_to_json(results):
             'gaze': {
                 'pitch_deg': float(np.degrees(r['gaze_pitch'])) if 'gaze_pitch' in r else None,
                 'yaw_deg': float(np.degrees(r['gaze_yaw'])) if 'gaze_yaw' in r else None
+            },
+            'head_pose': {
+                'pitch': float(r['head_pitch']) if 'head_pitch' in r else None,
+                'yaw': float(r['head_yaw']) if 'head_yaw' in r else None,
+                'roll': float(r['head_roll']) if 'head_roll' in r else None
             }
         }
         output.append(item)
@@ -291,3 +301,4 @@ with open('results.json', 'w') as f:
 - [Face Search](face-search.md) - Build a search system
 - [Detection Module](../modules/detection.md) - Detection options
 - [Recognition Module](../modules/recognition.md) - Recognition details
+- [Head Pose Module](../modules/headpose.md) - Head orientation estimation

docs/recipes/video-webcam.md

@@ -26,12 +26,7 @@ while True:
 
     faces = detector.detect(frame)
 
-    draw_detections(
-        image=frame,
-        bboxes=[f.bbox for f in faces],
-        scores=[f.confidence for f in faces],
-        landmarks=[f.landmarks for f in faces]
-    )
+    draw_detections(image=frame, faces=faces)
 
     cv2.imshow("Face Detection", frame)
 
@@ -175,3 +170,4 @@ while True:
 - [Batch Processing](batch-processing.md) - Process multiple files
 - [Detection Module](../modules/detection.md) - Detection options
 - [Gaze Module](../modules/gaze.md) - Gaze estimation
+- [Head Pose Module](../modules/headpose.md) - Head orientation estimation

examples/11_head_pose_estimation.ipynb

@@ -0,0 +1,223 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Head Pose Estimation with UniFace\n",
+    "\n",
+    "<div style=\"display:flex; flex-wrap:wrap; align-items:center;\">\n",
+    "  <a style=\"margin-right:10px; margin-bottom:6px;\" href=\"https://pepy.tech/projects/uniface\"><img alt=\"PyPI Downloads\" src=\"https://static.pepy.tech/personalized-badge/uniface?period=total&units=international_system&left_color=grey&right_color=blue&left_text=Downloads\"></a>\n",
+    "  <a style=\"margin-right:10px; margin-bottom:6px;\" href=\"https://pypi.org/project/uniface/\"><img alt=\"PyPI Version\" src=\"https://img.shields.io/pypi/v/uniface.svg\"></a>\n",
+    "  <a style=\"margin-right:10px; margin-bottom:6px;\" href=\"https://opensource.org/licenses/MIT\"><img alt=\"License\" src=\"https://img.shields.io/badge/License-MIT-blue.svg\"></a>\n",
+    "  <a style=\"margin-bottom:6px;\" href=\"https://github.com/yakhyo/uniface\"><img alt=\"GitHub Stars\" src=\"https://img.shields.io/github/stars/yakhyo/uniface.svg?style=social\"></a>\n",
+    "</div>\n",
+    "\n",
+    "**UniFace** is a lightweight, production-ready Python library for face detection, recognition, tracking, landmark analysis, face parsing, gaze estimation, and face attributes.\n",
+    "\n",
+    "🔗 **GitHub**: [github.com/yakhyo/uniface](https://github.com/yakhyo/uniface) | 📚 **Docs**: [yakhyo.github.io/uniface](https://yakhyo.github.io/uniface)\n",
+    "\n",
+    "---\n",
+    "\n",
+    "This notebook demonstrates head pose estimation using the **UniFace** library.\n",
+    "\n",
+    "## 1. Install UniFace"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -q \"uniface[cpu]\"\n",
+    "\n",
+    "# Clone repo for assets (Colab only)\n",
+    "import os\n",
+    "if 'COLAB_GPU' in os.environ or 'COLAB_RELEASE_TAG' in os.environ:\n",
+    "    if not os.path.exists('uniface'):\n",
+    "        !git clone --depth 1 https://github.com/yakhyo/uniface.git\n",
+    "    os.chdir('uniface/examples')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Import Libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import cv2\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from pathlib import Path\n",
+    "from PIL import Image\n",
+    "\n",
+    "import uniface\n",
+    "from uniface.detection import RetinaFace\n",
+    "from uniface.headpose import HeadPose\n",
+    "from uniface.draw import draw_head_pose\n",
+    "\n",
+    "print(f\"UniFace version: {uniface.__version__}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Initialize Models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Initialize face detector\n",
+    "detector = RetinaFace(confidence_threshold=0.5)\n",
+    "\n",
+    "# Initialize head pose estimator (default: ResNet18 backbone)\n",
+    "head_pose = HeadPose()\n",
+    "\n",
+    "print(\"Models initialized successfully!\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4. Process All Test Images\n",
+    "\n",
+    "Display original images in the first row and head-pose-annotated images in the second row."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get all test images\n",
+    "test_images_dir = Path('../assets/test_images')\n",
+    "test_images = sorted(test_images_dir.glob('*.jpg'))\n",
+    "\n",
+    "original_images = []\n",
+    "annotated_images = []\n",
+    "\n",
+    "for img_path in test_images:\n",
+    "    image = cv2.imread(str(img_path))\n",
+    "    if image is None:\n",
+    "        continue\n",
+    "\n",
+    "    # Store original (BGR -> RGB for display)\n",
+    "    original_images.append(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))\n",
+    "\n",
+    "    # Detect faces and estimate head pose\n",
+    "    faces = detector.detect(image)\n",
+    "\n",
+    "    for face in faces:\n",
+    "        x1, y1, x2, y2 = map(int, face.bbox)\n",
+    "        face_crop = image[y1:y2, x1:x2]\n",
+    "\n",
+    "        if face_crop.size == 0:\n",
+    "            continue\n",
+    "\n",
+    "        result = head_pose.estimate(face_crop)\n",
+    "        draw_head_pose(image, face.bbox, result.pitch, result.yaw, result.roll)\n",
+    "\n",
+    "        print(f\"{img_path.name}: pitch={result.pitch:.1f}°, yaw={result.yaw:.1f}°, roll={result.roll:.1f}°\")\n",
+    "\n",
+    "    annotated_images.append(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))\n",
+    "\n",
+    "print(f\"\\nProcessed {len(original_images)} images\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5. Visualize Results\n",
+    "\n",
+    "**First row**: Original images  \n",
+    "**Second row**: Images with head pose 3D cube overlay"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "num_images = len(original_images)\n",
+    "\n",
+    "# Create figure with 2 rows\n",
+    "fig, axes = plt.subplots(2, num_images, figsize=(5 * num_images, 10))\n",
+    "\n",
+    "if num_images == 1:\n",
+    "    axes = axes.reshape(2, 1)\n",
+    "\n",
+    "for i in range(num_images):\n",
+    "    axes[0, i].imshow(original_images[i])\n",
+    "    axes[0, i].set_title('Original', fontsize=12)\n",
+    "    axes[0, i].axis('off')\n",
+    "\n",
+    "    axes[1, i].imshow(annotated_images[i])\n",
+    "    axes[1, i].set_title('Head Pose', fontsize=12)\n",
+    "    axes[1, i].axis('off')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Notes\n",
+    "\n",
+    "- **Input**: Head pose estimation requires a face crop (obtained from face detection)\n",
+    "- **Output**: `HeadPoseResult` with pitch, yaw, and roll angles in **degrees**\n",
+    "- **Visualization**: Two modes available — `'cube'` (3D wireframe) and `'axis'` (X/Y/Z coordinate axes)\n",
+    "- **Models**: 6 backbone variants available via `HeadPoseWeights` enum\n",
+    "- **Method**: Uses 6D rotation representation converted to Euler angles\n",
+    "\n",
+    "### Available Backbones\n",
+    "\n",
+    "```python\n",
+    "from uniface.constants import HeadPoseWeights\n",
+    "\n",
+    "# Options: RESNET18, RESNET34, RESNET50, MOBILENET_V2, MOBILENET_V3_SMALL, MOBILENET_V3_LARGE\n",
+    "head_pose = HeadPose(model_name=HeadPoseWeights.RESNET50)\n",
+    "```"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

examples/12_face_recognition.ipynb

@@ -0,0 +1,356 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "0",
+   "metadata": {},
+   "source": [
+    "# Face Recognition: RetinaFace → Align → ArcFace\n",
+    "\n",
+    "<div style=\"display:flex; flex-wrap:wrap; align-items:center;\">\n",
+    "  <a style=\"margin-right:10px; margin-bottom:6px;\" href=\"https://pepy.tech/projects/uniface\"><img alt=\"PyPI Downloads\" src=\"https://static.pepy.tech/personalized-badge/uniface?period=total&units=international_system&left_color=grey&right_color=blue&left_text=Downloads\"></a>\n",
+    "  <a style=\"margin-right:10px; margin-bottom:6px;\" href=\"https://pypi.org/project/uniface/\"><img alt=\"PyPI Version\" src=\"https://img.shields.io/pypi/v/uniface.svg\"></a>\n",
+    "  <a style=\"margin-right:10px; margin-bottom:6px;\" href=\"https://opensource.org/licenses/MIT\"><img alt=\"License\" src=\"https://img.shields.io/badge/License-MIT-blue.svg\"></a>\n",
+    "  <a style=\"margin-bottom:6px;\" href=\"https://github.com/yakhyo/uniface\"><img alt=\"GitHub Stars\" src=\"https://img.shields.io/github/stars/yakhyo/uniface.svg?style=social\"></a>\n",
+    "</div>\n",
+    "\n",
+    "**UniFace** is a lightweight, production-ready Python library for face detection, recognition, tracking, landmark analysis, face parsing, gaze estimation, and face attributes.\n",
+    "\n",
+    "🔗 **GitHub**: [github.com/yakhyo/uniface](https://github.com/yakhyo/uniface) | 📚 **Docs**: [yakhyo.github.io/uniface](https://yakhyo.github.io/uniface)\n",
+    "\n",
+    "---\n",
+    "\n",
+    "This notebook demonstrates face recognition **without** the high-level `FaceAnalyzer` wrapper. Each step is handled manually:\n",
+    "\n",
+    "1. **RetinaFace**: Detects faces and extracts 5-point landmarks.\n",
+    "2. **Face Alignment**: Warps each face into a standardized 112x112 crop using the landmarks.\n",
+    "3. **ArcFace**: Generates a 512-D L2-normalized embedding from the aligned crop.\n",
+    "\n",
+    "We compare three test images: `image0.jpg`, `image1.jpg`, and `image5.jpg`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1",
+   "metadata": {},
+   "source": [
+    "## 1. Install UniFace"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -q \"uniface[cpu]\"\n",
+    "\n",
+    "# Clone repo for assets (Colab only)\n",
+    "import os\n",
+    "if 'COLAB_GPU' in os.environ or 'COLAB_RELEASE_TAG' in os.environ:\n",
+    "    if not os.path.exists('uniface'):\n",
+    "        !git clone --depth 1 https://github.com/yakhyo/uniface.git\n",
+    "    os.chdir('uniface/examples')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3",
+   "metadata": {},
+   "source": [
+    "## 2. Import Libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import cv2\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.patches as patches\n",
+    "\n",
+    "import uniface\n",
+    "from uniface.detection import RetinaFace\n",
+    "from uniface.recognition import ArcFace\n",
+    "from uniface.face_utils import face_alignment\n",
+    "\n",
+    "print(f\"UniFace version: {uniface.__version__}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5",
+   "metadata": {},
+   "source": [
+    "## 3. Configuration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "IMAGE_PATHS = {\n",
+    "    \"image0\": \"../assets/test_images/image0.jpg\",\n",
+    "    \"image1\": \"../assets/test_images/image1.jpg\",\n",
+    "    \"image5\": \"../assets/test_images/image5.jpg\",\n",
+    "}\n",
+    "THRESHOLD = 0.4   # Cosine similarity threshold for \"same person\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7",
+   "metadata": {},
+   "source": [
+    "## 4. Initialize Models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "detector = RetinaFace(confidence_threshold=0.5)\n",
+    "recognizer = ArcFace()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9",
+   "metadata": {},
+   "source": [
+    "## 5. Load Images & Detect Faces\n",
+    "\n",
+    "We use the detector to find faces and their landmarks in each image."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "10",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "images = {}\n",
+    "faces = {}\n",
+    "\n",
+    "for name, path in IMAGE_PATHS.items():\n",
+    "    img = cv2.imread(path)\n",
+    "    if img is None:\n",
+    "        raise FileNotFoundError(f\"Cannot read: {path}\")\n",
+    "\n",
+    "    detected = detector.detect(img)\n",
+    "    if not detected:\n",
+    "        raise RuntimeError(f\"No face detected in: {path}\")\n",
+    "\n",
+    "    images[name] = img\n",
+    "    faces[name] = detected[0]   # Keep highest-confidence face\n",
+    "    print(f\"{name:8s} | {len(detected)} face(s) detected | confidence={faces[name].confidence:.3f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "11",
+   "metadata": {},
+   "source": [
+    "## 6. Visualize Detections"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "12",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "LM_COLORS = [\"red\", \"blue\", \"green\", \"cyan\", \"magenta\"]\n",
+    "\n",
+    "fig, axes = plt.subplots(1, 3, figsize=(15, 5))\n",
+    "fig.suptitle(\"Detected Faces & 5-Point Landmarks\", fontweight=\"bold\", fontsize=16)\n",
+    "\n",
+    "for ax, (name, img) in zip(axes, images.items()):\n",
+    "    face = faces[name]\n",
+    "    ax.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))\n",
+    "    ax.set_title(f\"{name}\\nconf={face.confidence:.3f}\", fontsize=12)\n",
+    "    ax.axis(\"off\")\n",
+    "\n",
+    "    # Bounding box\n",
+    "    x1, y1, x2, y2 = face.bbox.astype(int)\n",
+    "    ax.add_patch(patches.Rectangle(\n",
+    "        (x1, y1), x2 - x1, y2 - y1,\n",
+    "        linewidth=2, edgecolor=\"lime\", facecolor=\"none\"))\n",
+    "\n",
+    "    # Landmarks\n",
+    "    for (lx, ly), c in zip(face.landmarks, LM_COLORS):\n",
+    "        ax.plot(lx, ly, \"o\", color=c, markersize=6)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "13",
+   "metadata": {},
+   "source": [
+    "## 7. Face Alignment\n",
+    "\n",
+    "We warp the detected faces into a standardized 112x112 size. This improves recognition accuracy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "14",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "aligned = {}\n",
+    "\n",
+    "for name, img in images.items():\n",
+    "    lm = faces[name].landmarks\n",
+    "    crop, _ = face_alignment(img, lm, image_size=(112, 112))\n",
+    "    aligned[name] = crop\n",
+    "\n",
+    "fig, axes = plt.subplots(1, 3, figsize=(12, 4))\n",
+    "fig.suptitle(\"Aligned Face Crops (112x112)\", fontweight=\"bold\", fontsize=14)\n",
+    "\n",
+    "for ax, (name, crop) in zip(axes, aligned.items()):\n",
+    "    ax.imshow(cv2.cvtColor(crop, cv2.COLOR_BGR2RGB))\n",
+    "    ax.set_title(name, fontsize=12)\n",
+    "    ax.axis(\"off\")\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "15",
+   "metadata": {},
+   "source": [
+    "## 8. Extract Embeddings\n",
+    "\n",
+    "We pass the aligned crops to ArcFace to get the 512-D vectors."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "16",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "embeddings = {}\n",
+    "\n",
+    "for name, crop in aligned.items():\n",
+    "    # landmarks=None because image is already aligned\n",
+    "    emb = recognizer.get_normalized_embedding(crop, landmarks=None)\n",
+    "    embeddings[name] = emb\n",
+    "    print(f\"{name:8s} | embedding shape={emb.shape} | L2-norm={np.linalg.norm(emb):.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "17",
+   "metadata": {},
+   "source": [
+    "## 9. Pairwise Cosine Similarity\n",
+    "\n",
+    "Since embeddings are normalized, cosine similarity is just the dot product."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "18",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "names = list(embeddings.keys())\n",
+    "n = len(names)\n",
+    "sim_matrix = np.zeros((n, n))\n",
+    "\n",
+    "for i, ni in enumerate(names):\n",
+    "    for j, nj in enumerate(names):\n",
+    "        # Use squeeze() to handle (1, 512) shapes if present\n",
+    "        sim_matrix[i, j] = float(np.dot(embeddings[ni].squeeze(), embeddings[nj].squeeze()))\n",
+    "\n",
+    "# Print comparison results\n",
+    "pairs = [(names[i], names[j]) for i in range(n) for j in range(i + 1, n)]\n",
+    "for a, b in pairs:\n",
+    "    s = float(np.dot(embeddings[a].squeeze(), embeddings[b].squeeze()))\n",
+    "    verdict = \"✓ Same person\" if s >= THRESHOLD else \"✗ Different people\"\n",
+    "    print(f\"{a} vs {b}: similarity={s:.4f} → {verdict}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "19",
+   "metadata": {},
+   "source": [
+    "## 10. Similarity Heatmap"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "20",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(8, 6))\n",
+    "im = ax.imshow(sim_matrix, vmin=0, vmax=1, cmap=\"viridis\")\n",
+    "plt.colorbar(im, ax=ax, label=\"Cosine similarity\")\n",
+    "\n",
+    "ax.set_xticks(range(n))\n",
+    "ax.set_yticks(range(n))\n",
+    "ax.set_xticklabels(names, rotation=30, ha=\"right\")\n",
+    "ax.set_yticklabels(names)\n",
+    "ax.set_title(\"Pairwise Face Similarity (ArcFace)\", fontweight=\"bold\")\n",
+    "\n",
+    "for i in range(n):\n",
+    "    for j in range(n):\n",
+    "        val = sim_matrix[i, j]\n",
+    "        ax.text(j, i, f\"{val:.2f}\",\n",
+    "                ha=\"center\", va=\"center\",\n",
+    "                color=\"black\" if val >= 0.6 else \"white\",\n",
+    "                fontsize=12, fontweight=\"bold\")\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

examples/13_portrait_matting.ipynb

@@ -0,0 +1,265 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Portrait Matting with MODNet\n",
+    "\n",
+    "<div style=\"display:flex; flex-wrap:wrap; align-items:center;\">\n",
+    "  <a style=\"margin-right:10px; margin-bottom:6px;\" href=\"https://pepy.tech/projects/uniface\"><img alt=\"PyPI Downloads\" src=\"https://static.pepy.tech/personalized-badge/uniface?period=total&units=international_system&left_color=grey&right_color=blue&left_text=Downloads\"></a>\n",
+    "  <a style=\"margin-right:10px; margin-bottom:6px;\" href=\"https://pypi.org/project/uniface/\"><img alt=\"PyPI Version\" src=\"https://img.shields.io/pypi/v/uniface.svg\"></a>\n",
+    "  <a style=\"margin-right:10px; margin-bottom:6px;\" href=\"https://opensource.org/licenses/MIT\"><img alt=\"License\" src=\"https://img.shields.io/badge/License-MIT-blue.svg\"></a>\n",
+    "  <a style=\"margin-bottom:6px;\" href=\"https://github.com/yakhyo/uniface\"><img alt=\"GitHub Stars\" src=\"https://img.shields.io/github/stars/yakhyo/uniface.svg?style=social\"></a>\n",
+    "</div>\n",
+    "\n",
+    "**UniFace** is a lightweight, production-ready Python library for face detection, recognition, tracking, landmark analysis, face parsing, gaze estimation, and face attributes.\n",
+    "\n",
+    "🔗 **GitHub**: [github.com/yakhyo/uniface](https://github.com/yakhyo/uniface) | 📚 **Docs**: [yakhyo.github.io/uniface](https://yakhyo.github.io/uniface)\n",
+    "\n",
+    "---\n",
+    "\n",
+    "This notebook demonstrates portrait matting using **MODNet** — a trimap-free model that produces soft alpha mattes from full images. No face detection or cropping required.\n",
+    "\n",
+    "## 1. Install UniFace"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -q \"uniface[cpu]\"\n",
+    "\n",
+    "# Clone repo for assets (Colab only)\n",
+    "import os\n",
+    "if 'COLAB_GPU' in os.environ or 'COLAB_RELEASE_TAG' in os.environ:\n",
+    "    if not os.path.exists('uniface'):\n",
+    "        !git clone --depth 1 https://github.com/yakhyo/uniface.git\n",
+    "    os.chdir('uniface/examples')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Import Libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import cv2\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "import uniface\n",
+    "from uniface.matting import MODNet\n",
+    "\n",
+    "print(f\"UniFace version: {uniface.__version__}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Initialize Model\n",
+    "\n",
+    "MODNet has two variants:\n",
+    "- **PHOTOGRAPHIC** (default): optimized for high-quality portrait photos\n",
+    "- **WEBCAM**: optimized for real-time webcam feeds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "matting = MODNet()\n",
+    "\n",
+    "print(f\"Input size: {matting.input_size}\")\n",
+    "print(f\"Input name: {matting.input_name}\")\n",
+    "print(f\"Output names: {matting.output_names}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4. Helper Functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def compose(image, matte, background=None):\n",
+    "    \"\"\"Composite foreground over a background using the alpha matte.\"\"\"\n",
+    "    h, w = image.shape[:2]\n",
+    "    matte_3ch = matte[:, :, np.newaxis]\n",
+    "\n",
+    "    if background is None:\n",
+    "        bg = np.full_like(image, (0, 177, 64), dtype=np.uint8)\n",
+    "    else:\n",
+    "        bg = cv2.resize(background, (w, h), interpolation=cv2.INTER_AREA)\n",
+    "\n",
+    "    return (image * matte_3ch + bg * (1 - matte_3ch)).astype(np.uint8)\n",
+    "\n",
+    "\n",
+    "def show_results(image, matte):\n",
+    "    \"\"\"Display original, matte, and green screen as a single merged image.\"\"\"\n",
+    "    matte_vis = cv2.cvtColor((matte * 255).astype(np.uint8), cv2.COLOR_GRAY2BGR)\n",
+    "    green = compose(image, matte)\n",
+    "    merged = np.hstack([image, matte_vis, green])\n",
+    "\n",
+    "    plt.figure(figsize=(18, 6))\n",
+    "    plt.imshow(cv2.cvtColor(merged, cv2.COLOR_BGR2RGB))\n",
+    "    plt.axis(\"off\")\n",
+    "    plt.tight_layout()\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5. Basic Matting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "image = cv2.imread(\"../assets/demos/src_portrait1.jpg\")\n",
+    "print(f\"Image shape: {image.shape}\")\n",
+    "\n",
+    "matte = matting.predict(image)\n",
+    "print(f\"Matte shape: {matte.shape}\")\n",
+    "print(f\"Matte dtype: {matte.dtype}\")\n",
+    "print(f\"Matte range: [{matte.min():.3f}, {matte.max():.3f}]\")\n",
+    "\n",
+    "show_results(image, matte)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 6. Transparent Background (RGBA)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "alpha = (matte * 255).astype(np.uint8)\n",
+    "rgba = cv2.cvtColor(image, cv2.COLOR_BGR2BGRA)\n",
+    "rgba[:, :, 3] = alpha\n",
+    "\n",
+    "# Checkerboard background to visualize transparency\n",
+    "h, w = image.shape[:2]\n",
+    "checker = np.zeros((h, w, 3), dtype=np.uint8)\n",
+    "block = 20\n",
+    "for y in range(0, h, block):\n",
+    "    for x in range(0, w, block):\n",
+    "        if (y // block + x // block) % 2 == 0:\n",
+    "            checker[y:y+block, x:x+block] = 200\n",
+    "        else:\n",
+    "            checker[y:y+block, x:x+block] = 255\n",
+    "\n",
+    "matte_3ch = matte[:, :, np.newaxis]\n",
+    "rgba_vis = (image * matte_3ch + checker * (1 - matte_3ch)).astype(np.uint8)\n",
+    "\n",
+    "merged = np.hstack([image, rgba_vis])\n",
+    "\n",
+    "plt.figure(figsize=(16, 5))\n",
+    "plt.imshow(cv2.cvtColor(merged, cv2.COLOR_BGR2RGB))\n",
+    "plt.axis(\"off\")\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "print(f\"RGBA shape: {rgba.shape}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 7. Custom Background"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a gradient background\n",
+    "h, w = image.shape[:2]\n",
+    "gradient = np.zeros((h, w, 3), dtype=np.uint8)\n",
+    "for y in range(h):\n",
+    "    ratio = y / h\n",
+    "    gradient[y, :] = [int(180 * (1 - ratio)), int(100 + 80 * ratio), int(220 * ratio)]\n",
+    "\n",
+    "custom_bg = compose(image, matte, gradient)\n",
+    "green_bg = compose(image, matte)\n",
+    "\n",
+    "merged = np.hstack([image, green_bg, custom_bg])\n",
+    "\n",
+    "plt.figure(figsize=(18, 6))\n",
+    "plt.imshow(cv2.cvtColor(merged, cv2.COLOR_BGR2RGB))\n",
+    "plt.axis(\"off\")\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "MODNet provides trimap-free portrait matting:\n",
+    "\n",
+    "- **`predict(image)`** — returns `(H, W)` float32 alpha matte in `[0, 1]`\n",
+    "- **No face detection needed** — works on full images directly\n",
+    "- **Two variants** — `PHOTOGRAPHIC` for photos, `WEBCAM` for real-time\n",
+    "- **Compositing** — use the matte for transparent PNGs, green screen, or custom backgrounds\n",
+    "\n",
+    "For more details, see the [Matting docs](https://yakhyo.github.io/uniface/modules/matting/)."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

mkdocs.yml

@@ -1,5 +1,5 @@
 site_name: UniFace
-site_description: All-in-One Face Analysis Library with ONNX Runtime
+site_description: A Unified Face Analysis Library for Python
 site_author: Yakhyokhuja Valikhujaev
 site_url: https://yakhyo.github.io/uniface
 
@@ -150,10 +150,12 @@ nav:
       - Landmarks: modules/landmarks.md
       - Attributes: modules/attributes.md
       - Parsing: modules/parsing.md
+      - Matting: modules/matting.md
       - Gaze: modules/gaze.md
+      - Head Pose: modules/headpose.md
       - Anti-Spoofing: modules/spoofing.md
       - Privacy: modules/privacy.md
-      - Indexing: modules/indexing.md
+      - Stores: modules/stores.md
   - Guides:
       - Overview: concepts/overview.md
       - Inputs & Outputs: concepts/inputs-outputs.md

pyproject.toml

@@ -1,7 +1,7 @@
 [project]
 name = "uniface"
-version = "3.1.0"
-description = "UniFace: A Comprehensive Library for Face Detection, Recognition, Tracking, Landmark Analysis, Face Parsing, Gaze Estimation, Age, and Gender Detection"
+version = "3.5.3"
+description = "UniFace: A Unified Face Analysis Library for Python"
 readme = "README.md"
 license = "MIT"
 authors = [{ name = "Yakhyokhuja Valikhujaev", email = "yakhyo9696@gmail.com" }]
@@ -9,7 +9,7 @@ maintainers = [
     { name = "Yakhyokhuja Valikhujaev", email = "yakhyo9696@gmail.com" },
 ]
 
-requires-python = ">=3.10,<3.14"
+requires-python = ">=3.10,<3.15"
 keywords = [
     "face-detection",
     "face-recognition",
@@ -38,21 +38,28 @@ classifiers = [
     "Programming Language :: Python :: 3.11",
     "Programming Language :: Python :: 3.12",
     "Programming Language :: Python :: 3.13",
+    "Programming Language :: Python :: 3.14",
 ]
 
 dependencies = [
     "numpy>=1.21.0",
     "opencv-python>=4.5.0",
-    "onnxruntime>=1.16.0",
-    "scikit-image>=0.19.0",
+    "scikit-image>=0.22.0",
     "scipy>=1.7.0",
     "requests>=2.28.0",
     "tqdm>=4.64.0",
 ]
 
 [project.optional-dependencies]
-dev = ["pytest>=7.0.0", "ruff>=0.4.0"]
+cpu = ["onnxruntime>=1.16.0"]
 gpu = ["onnxruntime-gpu>=1.16.0"]
+dev = ["pytest>=7.0.0", "ruff>=0.4.0", "pre-commit>=3.0.0"]
+docs = [
+    "mkdocs-material>=9.0",
+    "pymdown-extensions>=10.0",
+    "mkdocs-git-committers-plugin-2>=1.0",
+    "mkdocs-git-revision-date-localized-plugin>=2.0",
+]
 
 [project.urls]
 Homepage = "https://github.com/yakhyo/uniface"

requirements.txt

@@ -1,7 +1,9 @@
 numpy>=1.21.0
 opencv-python>=4.5.0
-onnxruntime>=1.16.0
-scikit-image>=0.19.0
+scikit-image>=0.22.0
 scipy>=1.7.0
 requests>=2.28.0
 tqdm>=4.64.0
+# Install ONE of the following (not both):
+# onnxruntime>=1.16.0        # CPU / Apple Silicon  →  pip install uniface[cpu]
+# onnxruntime-gpu>=1.16.0    # NVIDIA CUDA          →  pip install uniface[gpu]

tests/test_age_gender.py

@@ -9,6 +9,14 @@ import numpy as np
 import pytest
 
 from uniface.attribute import AgeGender, AttributeResult
+from uniface.types import Face
+
+
+def _make_face(bbox: list[int] | np.ndarray) -> Face:
+    """Helper: build a minimal Face from a bounding box."""
+    bbox = np.asarray(bbox)
+    landmarks = np.zeros((5, 2), dtype=np.float32)
+    return Face(bbox=bbox, confidence=0.99, landmarks=landmarks)
 
 
 @pytest.fixture
@@ -22,30 +30,30 @@ def mock_image():
 
 
 @pytest.fixture
-def mock_bbox():
-    return [100, 100, 300, 300]
+def mock_face():
+    return _make_face([100, 100, 300, 300])
 
 
 def test_model_initialization(age_gender_model):
     assert age_gender_model is not None, 'AgeGender model initialization failed.'
 
 
-def test_prediction_output_format(age_gender_model, mock_image, mock_bbox):
-    result = age_gender_model.predict(mock_image, mock_bbox)
+def test_prediction_output_format(age_gender_model, mock_image, mock_face):
+    result = age_gender_model.predict(mock_image, mock_face)
     assert isinstance(result, AttributeResult), f'Result should be AttributeResult, got {type(result)}'
     assert isinstance(result.gender, int), f'Gender should be int, got {type(result.gender)}'
     assert isinstance(result.age, int), f'Age should be int, got {type(result.age)}'
     assert isinstance(result.sex, str), f'Sex should be str, got {type(result.sex)}'
 
 
-def test_gender_values(age_gender_model, mock_image, mock_bbox):
-    result = age_gender_model.predict(mock_image, mock_bbox)
+def test_gender_values(age_gender_model, mock_image, mock_face):
+    result = age_gender_model.predict(mock_image, mock_face)
     assert result.gender in [0, 1], f'Gender should be 0 (Female) or 1 (Male), got {result.gender}'
     assert result.sex in ['Female', 'Male'], f'Sex should be Female or Male, got {result.sex}'
 
 
-def test_age_range(age_gender_model, mock_image, mock_bbox):
-    result = age_gender_model.predict(mock_image, mock_bbox)
+def test_age_range(age_gender_model, mock_image, mock_face):
+    result = age_gender_model.predict(mock_image, mock_face)
     assert 0 <= result.age <= 120, f'Age should be between 0 and 120, got {result.age}'
 
 
@@ -57,39 +65,52 @@ def test_different_bbox_sizes(age_gender_model, mock_image):
     ]
 
     for bbox in test_bboxes:
-        result = age_gender_model.predict(mock_image, bbox)
+        face = _make_face(bbox)
+        result = age_gender_model.predict(mock_image, face)
         assert result.gender in [0, 1], f'Failed for bbox {bbox}'
         assert 0 <= result.age <= 120, f'Age out of range for bbox {bbox}'
 
 
-def test_different_image_sizes(age_gender_model, mock_bbox):
+def test_different_image_sizes(age_gender_model):
     test_sizes = [(480, 640, 3), (720, 1280, 3), (1080, 1920, 3)]
+    face = _make_face([100, 100, 300, 300])
 
     for size in test_sizes:
         mock_image = np.random.randint(0, 255, size, dtype=np.uint8)
-        result = age_gender_model.predict(mock_image, mock_bbox)
+        result = age_gender_model.predict(mock_image, face)
         assert result.gender in [0, 1], f'Failed for image size {size}'
         assert 0 <= result.age <= 120, f'Age out of range for image size {size}'
 
 
-def test_consistency(age_gender_model, mock_image, mock_bbox):
-    result1 = age_gender_model.predict(mock_image, mock_bbox)
-    result2 = age_gender_model.predict(mock_image, mock_bbox)
+def test_consistency(age_gender_model, mock_image, mock_face):
+    result1 = age_gender_model.predict(mock_image, mock_face)
+    result2 = age_gender_model.predict(mock_image, mock_face)
 
     assert result1.gender == result2.gender, 'Same input should produce same gender prediction'
     assert result1.age == result2.age, 'Same input should produce same age prediction'
 
 
+def test_face_enrichment(age_gender_model, mock_image, mock_face):
+    """predict() must write gender & age back to the Face object."""
+    assert mock_face.gender is None
+    assert mock_face.age is None
+
+    result = age_gender_model.predict(mock_image, mock_face)
+
+    assert mock_face.gender == result.gender
+    assert mock_face.age == result.age
+
+
 def test_bbox_list_format(age_gender_model, mock_image):
-    bbox_list = [100, 100, 300, 300]
-    result = age_gender_model.predict(mock_image, bbox_list)
+    face = _make_face([100, 100, 300, 300])
+    result = age_gender_model.predict(mock_image, face)
     assert result.gender in [0, 1], 'Should work with bbox as list'
     assert 0 <= result.age <= 120, 'Age should be in valid range'
 
 
 def test_bbox_array_format(age_gender_model, mock_image):
-    bbox_array = np.array([100, 100, 300, 300])
-    result = age_gender_model.predict(mock_image, bbox_array)
+    face = _make_face(np.array([100, 100, 300, 300]))
+    result = age_gender_model.predict(mock_image, face)
     assert result.gender in [0, 1], 'Should work with bbox as numpy array'
     assert 0 <= result.age <= 120, 'Age should be in valid range'
 
@@ -103,7 +124,8 @@ def test_multiple_predictions(age_gender_model, mock_image):
 
     results = []
     for bbox in bboxes:
-        result = age_gender_model.predict(mock_image, bbox)
+        face = _make_face(bbox)
+        result = age_gender_model.predict(mock_image, face)
         results.append(result)
 
     assert len(results) == 3, 'Should have 3 predictions'
@@ -112,28 +134,26 @@ def test_multiple_predictions(age_gender_model, mock_image):
         assert 0 <= result.age <= 120
 
 
-def test_age_is_positive(age_gender_model, mock_image, mock_bbox):
+def test_age_is_positive(age_gender_model, mock_image, mock_face):
     for _ in range(5):
-        result = age_gender_model.predict(mock_image, mock_bbox)
+        result = age_gender_model.predict(mock_image, mock_face)
         assert result.age >= 0, f'Age should be non-negative, got {result.age}'
 
 
-def test_output_format_for_visualization(age_gender_model, mock_image, mock_bbox):
-    result = age_gender_model.predict(mock_image, mock_bbox)
+def test_output_format_for_visualization(age_gender_model, mock_image, mock_face):
+    result = age_gender_model.predict(mock_image, mock_face)
     text = f'{result.sex}, {result.age}y'
     assert isinstance(text, str), 'Should be able to format as string'
     assert 'Male' in text or 'Female' in text, 'Text should contain gender'
     assert 'y' in text, "Text should contain 'y' for years"
 
 
-def test_attribute_result_fields(age_gender_model, mock_image, mock_bbox):
+def test_attribute_result_fields(age_gender_model, mock_image, mock_face):
     """Test that AttributeResult has correct fields for AgeGender model."""
-    result = age_gender_model.predict(mock_image, mock_bbox)
+    result = age_gender_model.predict(mock_image, mock_face)
 
-    # AgeGender should set gender and age
     assert result.gender is not None
     assert result.age is not None
 
-    # AgeGender should NOT set race and age_group (FairFace only)
     assert result.race is None
     assert result.age_group is None

tests/test_factory.py

@@ -9,12 +9,14 @@ import numpy as np
 import pytest
 
 from uniface import (
+    create_attribute_predictor,
     create_detector,
     create_landmarker,
     create_recognizer,
     list_available_detectors,
 )
-from uniface.constants import RetinaFaceWeights, SCRFDWeights
+from uniface.attribute import AgeGender, FairFace
+from uniface.constants import AgeGenderWeights, FairFaceWeights, RetinaFaceWeights, SCRFDWeights
 from uniface.spoofing import MiniFASNet, create_spoofer
 
 
@@ -89,6 +91,12 @@ def test_create_recognizer_sphereface():
     assert recognizer is not None, 'Failed to create SphereFace recognizer'
 
 
+def test_create_recognizer_edgeface():
+    """Test creating an EdgeFace recognizer using factory function."""
+    recognizer = create_recognizer('edgeface')
+    assert recognizer is not None, 'Failed to create EdgeFace recognizer'
+
+
 def test_create_recognizer_invalid_method():
     """
     Test that invalid recognizer method raises an error.
@@ -165,7 +173,7 @@ def test_recognizer_inference_from_factory():
 
     embedding = recognizer.get_embedding(mock_image)
     assert embedding is not None, 'Recognizer should return embedding'
-    assert embedding.shape[1] == 512, 'Should return 512-dimensional embedding'
+    assert embedding.shape == (1, 512), 'get_embedding should return (1, 512) with batch dimension'
 
 
 def test_landmarker_inference_from_factory():
@@ -236,3 +244,19 @@ def test_create_spoofer_with_providers():
     """Test that create_spoofer forwards providers kwarg without TypeError."""
     spoofer = create_spoofer(providers=['CPUExecutionProvider'])
     assert isinstance(spoofer, MiniFASNet), 'Should return MiniFASNet instance'
+
+
+# create_attribute_predictor tests
+def test_create_attribute_predictor_age_gender():
+    predictor = create_attribute_predictor(AgeGenderWeights.DEFAULT)
+    assert isinstance(predictor, AgeGender), 'Should return AgeGender instance'
+
+
+def test_create_attribute_predictor_fairface():
+    predictor = create_attribute_predictor(FairFaceWeights.DEFAULT)
+    assert isinstance(predictor, FairFace), 'Should return FairFace instance'
+
+
+def test_create_attribute_predictor_invalid():
+    with pytest.raises(ValueError, match='Unsupported attribute model'):
+        create_attribute_predictor('invalid_model')

tests/test_headpose.py

@@ -0,0 +1,115 @@
+# Copyright 2025-2026 Yakhyokhuja Valikhujaev
+# Author: Yakhyokhuja Valikhujaev
+# GitHub: https://github.com/yakhyo
+
+
+from __future__ import annotations
+
+import numpy as np
+import pytest
+
+from uniface import HeadPose, HeadPoseResult, create_head_pose_estimator
+from uniface.headpose import BaseHeadPoseEstimator
+from uniface.headpose.models import HeadPose as HeadPoseModel
+
+
+def test_create_head_pose_estimator_default():
+    """Test creating a head pose estimator with default parameters."""
+    estimator = create_head_pose_estimator()
+    assert isinstance(estimator, HeadPose), 'Should return HeadPose instance'
+
+
+def test_create_head_pose_estimator_aliases():
+    """Test that factory accepts all documented aliases."""
+    for alias in ('headpose', 'head_pose', '6drepnet'):
+        estimator = create_head_pose_estimator(alias)
+        assert isinstance(estimator, HeadPose), f"Alias '{alias}' should return HeadPose"
+
+
+def test_create_head_pose_estimator_invalid():
+    """Test that invalid method raises ValueError."""
+    with pytest.raises(ValueError, match='Unsupported head pose estimation method'):
+        create_head_pose_estimator('invalid_method')
+
+
+def test_head_pose_inference():
+    """Test that HeadPose can run inference on a mock image."""
+    estimator = HeadPose()
+    mock_image = np.random.randint(0, 255, (224, 224, 3), dtype=np.uint8)
+    result = estimator.estimate(mock_image)
+
+    assert isinstance(result, HeadPoseResult), 'Should return HeadPoseResult'
+    assert isinstance(result.pitch, float), 'pitch should be float'
+    assert isinstance(result.yaw, float), 'yaw should be float'
+    assert isinstance(result.roll, float), 'roll should be float'
+
+
+def test_head_pose_callable():
+    """Test that HeadPose is callable via __call__."""
+    estimator = HeadPose()
+    mock_image = np.random.randint(0, 255, (224, 224, 3), dtype=np.uint8)
+    result = estimator(mock_image)
+
+    assert isinstance(result, HeadPoseResult), '__call__ should return HeadPoseResult'
+
+
+def test_head_pose_result_repr():
+    """Test HeadPoseResult repr formatting."""
+    result = HeadPoseResult(pitch=10.5, yaw=-20.3, roll=5.1)
+    repr_str = repr(result)
+    assert 'HeadPoseResult' in repr_str
+    assert '10.5' in repr_str
+    assert '-20.3' in repr_str
+    assert '5.1' in repr_str
+
+
+def test_head_pose_result_frozen():
+    """Test that HeadPoseResult is immutable."""
+    result = HeadPoseResult(pitch=1.0, yaw=2.0, roll=3.0)
+    with pytest.raises(AttributeError):
+        result.pitch = 99.0  # type: ignore[misc]
+
+
+def test_rotation_matrix_to_euler_identity():
+    """Test that identity rotation matrix gives zero angles."""
+    identity = np.eye(3).reshape(1, 3, 3)
+    euler = HeadPoseModel.rotation_matrix_to_euler(identity)
+
+    assert euler.shape == (1, 3), 'Should return (1, 3) shaped array'
+    np.testing.assert_allclose(euler[0], [0.0, 0.0, 0.0], atol=1e-5)
+
+
+def test_rotation_matrix_to_euler_90deg_yaw():
+    """Test 90-degree yaw rotation."""
+    angle = np.radians(90)
+    R = np.array(
+        [
+            [np.cos(angle), 0, np.sin(angle)],
+            [0, 1, 0],
+            [-np.sin(angle), 0, np.cos(angle)],
+        ]
+    ).reshape(1, 3, 3)
+    euler = HeadPoseModel.rotation_matrix_to_euler(R)
+
+    np.testing.assert_allclose(euler[0, 1], 90.0, atol=1e-3)
+
+
+def test_rotation_matrix_to_euler_batch():
+    """Test batch processing of rotation matrices."""
+    batch = np.stack([np.eye(3), np.eye(3), np.eye(3)], axis=0)
+    euler = HeadPoseModel.rotation_matrix_to_euler(batch)
+
+    assert euler.shape == (3, 3), 'Batch of 3 should return (3, 3)'
+    np.testing.assert_allclose(euler, 0.0, atol=1e-5)
+
+
+def test_factory_returns_correct_type():
+    """Test that factory function returns BaseHeadPoseEstimator subclass."""
+    estimator = create_head_pose_estimator()
+    assert isinstance(estimator, BaseHeadPoseEstimator), 'Should be BaseHeadPoseEstimator subclass'
+
+
+def test_head_pose_with_providers():
+    """Test that HeadPose accepts providers kwarg."""
+    estimator = HeadPose(providers=['CPUExecutionProvider'])
+    assert isinstance(estimator, HeadPose), 'Should create with explicit providers'

tests/test_matting.py

@@ -0,0 +1,158 @@
+# Copyright 2025-2026 Yakhyokhuja Valikhujaev
+# Author: Yakhyokhuja Valikhujaev
+# GitHub: https://github.com/yakhyo
+
+
+from __future__ import annotations
+
+import numpy as np
+import pytest
+
+from uniface.constants import MODNetWeights
+from uniface.matting import MODNet, create_matting_model
+
+
+def test_modnet_initialization():
+    """Test MODNet initialization with default weights."""
+    matting = MODNet()
+    assert matting is not None
+    assert matting.input_size == 512
+
+
+def test_modnet_with_webcam_weights():
+    """Test MODNet initialization with webcam variant."""
+    matting = MODNet(model_name=MODNetWeights.WEBCAM)
+    assert matting is not None
+    assert matting.input_size == 512
+
+
+def test_modnet_custom_input_size():
+    """Test MODNet with custom input size."""
+    matting = MODNet(input_size=256)
+    assert matting.input_size == 256
+
+
+def test_modnet_preprocess():
+    """Test preprocessing produces correct tensor shape and dtype."""
+    matting = MODNet()
+
+    image = np.random.randint(0, 255, (480, 640, 3), dtype=np.uint8)
+    tensor, orig_h, orig_w = matting.preprocess(image)
+
+    assert tensor.dtype == np.float32
+    assert tensor.ndim == 4
+    assert tensor.shape[0] == 1
+    assert tensor.shape[1] == 3
+    assert tensor.shape[2] % 32 == 0
+    assert tensor.shape[3] % 32 == 0
+    assert orig_h == 480
+    assert orig_w == 640
+
+
+def test_modnet_preprocess_small_image():
+    """Test preprocessing with image smaller than input_size."""
+    matting = MODNet(input_size=512)
+
+    image = np.random.randint(0, 255, (128, 128, 3), dtype=np.uint8)
+    tensor, orig_h, orig_w = matting.preprocess(image)
+
+    assert tensor.shape[2] % 32 == 0
+    assert tensor.shape[3] % 32 == 0
+    assert orig_h == 128
+    assert orig_w == 128
+
+
+def test_modnet_preprocess_large_image():
+    """Test preprocessing with image larger than input_size."""
+    matting = MODNet(input_size=512)
+
+    image = np.random.randint(0, 255, (1080, 1920, 3), dtype=np.uint8)
+    tensor, orig_h, orig_w = matting.preprocess(image)
+
+    assert tensor.shape[2] % 32 == 0
+    assert tensor.shape[3] % 32 == 0
+    assert orig_h == 1080
+    assert orig_w == 1920
+
+
+def test_modnet_postprocess():
+    """Test postprocessing resizes matte to original dimensions."""
+    matting = MODNet()
+
+    dummy_output = np.random.rand(1, 1, 512, 672).astype(np.float32)
+    matte = matting.postprocess(dummy_output, original_size=(640, 480))
+
+    assert matte.shape == (480, 640)
+    assert matte.dtype == np.float32
+
+
+def test_modnet_predict():
+    """Test end-to-end prediction."""
+    matting = MODNet()
+
+    image = np.random.randint(0, 255, (480, 640, 3), dtype=np.uint8)
+    matte = matting.predict(image)
+
+    assert matte.shape == (480, 640)
+    assert matte.dtype == np.float32
+    assert matte.min() >= 0.0
+    assert matte.max() <= 1.0
+
+
+def test_modnet_callable():
+    """Test that MODNet is callable via __call__."""
+    matting = MODNet()
+    image = np.random.randint(0, 255, (256, 256, 3), dtype=np.uint8)
+
+    matte = matting(image)
+
+    assert matte.shape == (256, 256)
+    assert matte.dtype == np.float32
+
+
+def test_modnet_different_input_sizes():
+    """Test prediction with various image dimensions."""
+    matting = MODNet()
+
+    sizes = [(256, 256), (480, 640), (720, 1280), (300, 500)]
+
+    for h, w in sizes:
+        image = np.random.randint(0, 255, (h, w, 3), dtype=np.uint8)
+        matte = matting.predict(image)
+
+        assert matte.shape == (h, w), f'Failed for size {h}x{w}'
+        assert matte.dtype == np.float32
+
+
+# Factory tests
+
+
+def test_create_matting_model_default():
+    """Test factory with default parameters."""
+    matting = create_matting_model()
+    assert matting is not None
+    assert isinstance(matting, MODNet)
+
+
+def test_create_matting_model_with_enum():
+    """Test factory with enum."""
+    matting = create_matting_model(MODNetWeights.WEBCAM)
+    assert isinstance(matting, MODNet)
+
+
+def test_create_matting_model_with_string():
+    """Test factory with string model name."""
+    matting = create_matting_model('modnet_photographic')
+    assert isinstance(matting, MODNet)
+
+
+def test_create_matting_model_webcam_string():
+    """Test factory with webcam string model name."""
+    matting = create_matting_model('modnet_webcam')
+    assert isinstance(matting, MODNet)
+
+
+def test_create_matting_model_invalid():
+    """Test factory with invalid model name."""
+    with pytest.raises(ValueError, match='Unknown matting model'):
+        create_matting_model('invalid_model')

tests/test_recognition.py

@@ -8,7 +8,7 @@ from __future__ import annotations
 import numpy as np
 import pytest
 
-from uniface.recognition import ArcFace, MobileFace, SphereFace
+from uniface.recognition import ArcFace, EdgeFace, MobileFace, SphereFace
 
 
 @pytest.fixture
@@ -35,6 +35,12 @@ def sphereface_model():
     return SphereFace()
 
 
+@pytest.fixture
+def edgeface_model():
+    """Fixture to initialize the EdgeFace model for testing."""
+    return EdgeFace()
+
+
 @pytest.fixture
 def mock_aligned_face():
     """
@@ -74,7 +80,7 @@ def test_arcface_embedding_shape(arcface_model, mock_aligned_face):
     """
     embedding = arcface_model.get_embedding(mock_aligned_face)
 
-    # ArcFace typically produces 512-dimensional embeddings
+    # ArcFace get_embedding returns raw ONNX output with batch dimension
     assert embedding.shape[1] == 512, f'Expected 512-dim embedding, got {embedding.shape[1]}'
     assert embedding.shape[0] == 1, 'Embedding should have batch dimension of 1'
 
@@ -88,7 +94,8 @@ def test_arcface_normalized_embedding(arcface_model, mock_landmarks):
 
     embedding = arcface_model.get_normalized_embedding(mock_image, mock_landmarks)
 
-    # Check that embedding is normalized (L2 norm ≈ 1.0)
+    # Check shape and normalization
+    assert embedding.shape == (512,), f'Expected shape (512,), got {embedding.shape}'
     norm = np.linalg.norm(embedding)
     assert np.isclose(norm, 1.0, atol=1e-5), f'Normalized embedding should have norm 1.0, got {norm}'
 
@@ -125,7 +132,7 @@ def test_mobileface_embedding_shape(mobileface_model, mock_aligned_face):
     """
     embedding = mobileface_model.get_embedding(mock_aligned_face)
 
-    # MobileFace typically produces 512-dimensional embeddings
+    # MobileFace get_embedding returns raw ONNX output with batch dimension
     assert embedding.shape[1] == 512, f'Expected 512-dim embedding, got {embedding.shape[1]}'
     assert embedding.shape[0] == 1, 'Embedding should have batch dimension of 1'
 
@@ -138,6 +145,7 @@ def test_mobileface_normalized_embedding(mobileface_model, mock_landmarks):
 
     embedding = mobileface_model.get_normalized_embedding(mock_image, mock_landmarks)
 
+    assert embedding.shape == (512,), f'Expected shape (512,), got {embedding.shape}'
     norm = np.linalg.norm(embedding)
     assert np.isclose(norm, 1.0, atol=1e-5), f'Normalized embedding should have norm 1.0, got {norm}'
 
@@ -156,7 +164,7 @@ def test_sphereface_embedding_shape(sphereface_model, mock_aligned_face):
     """
     embedding = sphereface_model.get_embedding(mock_aligned_face)
 
-    # SphereFace typically produces 512-dimensional embeddings
+    # SphereFace get_embedding returns raw ONNX output with batch dimension
     assert embedding.shape[1] == 512, f'Expected 512-dim embedding, got {embedding.shape[1]}'
     assert embedding.shape[0] == 1, 'Embedding should have batch dimension of 1'
 
@@ -169,10 +177,50 @@ def test_sphereface_normalized_embedding(sphereface_model, mock_landmarks):
 
     embedding = sphereface_model.get_normalized_embedding(mock_image, mock_landmarks)
 
+    assert embedding.shape == (512,), f'Expected shape (512,), got {embedding.shape}'
     norm = np.linalg.norm(embedding)
     assert np.isclose(norm, 1.0, atol=1e-5), f'Normalized embedding should have norm 1.0, got {norm}'
 
 
+# EdgeFace Tests
+def test_edgeface_initialization(edgeface_model):
+    """Test that the EdgeFace model initializes correctly."""
+    assert edgeface_model is not None, 'EdgeFace model initialization failed.'
+
+
+def test_edgeface_embedding_shape(edgeface_model, mock_aligned_face):
+    """Test that EdgeFace produces embeddings with the correct shape."""
+    embedding = edgeface_model.get_embedding(mock_aligned_face)
+
+    assert embedding.shape[1] == 512, f'Expected 512-dim embedding, got {embedding.shape[1]}'
+    assert embedding.shape[0] == 1, 'Embedding should have batch dimension of 1'
+
+
+def test_edgeface_normalized_embedding(edgeface_model, mock_landmarks):
+    """Test that EdgeFace normalized embeddings have unit length."""
+    mock_image = np.random.randint(0, 255, (640, 640, 3), dtype=np.uint8)
+
+    embedding = edgeface_model.get_normalized_embedding(mock_image, mock_landmarks)
+
+    assert embedding.shape == (512,), f'Expected shape (512,), got {embedding.shape}'
+    norm = np.linalg.norm(embedding)
+    assert np.isclose(norm, 1.0, atol=1e-5), f'Normalized embedding should have norm 1.0, got {norm}'
+
+
+def test_edgeface_embedding_dtype(edgeface_model, mock_aligned_face):
+    """Test that EdgeFace embeddings have the correct data type."""
+    embedding = edgeface_model.get_embedding(mock_aligned_face)
+    assert embedding.dtype == np.float32, f'Expected float32, got {embedding.dtype}'
+
+
+def test_edgeface_consistency(edgeface_model, mock_aligned_face):
+    """Test that the same input produces the same EdgeFace embedding."""
+    embedding1 = edgeface_model.get_embedding(mock_aligned_face)
+    embedding2 = edgeface_model.get_embedding(mock_aligned_face)
+
+    assert np.allclose(embedding1, embedding2), 'Same input should produce same embedding'
+
+
 # Cross-model comparison tests
 def test_different_models_different_embeddings(arcface_model, mobileface_model, mock_aligned_face):
     """

tools/README.md

@@ -12,9 +12,11 @@ CLI utilities for testing and running UniFace features.
 | `anonymize.py` | Face anonymization/blurring for privacy |
 | `emotion.py` | Emotion detection (7 or 8 emotions) |
 | `gaze.py` | Gaze direction estimation |
+| `headpose.py` | Head pose estimation (pitch, yaw, roll) |
 | `landmarks.py` | 106-point facial landmark detection |
 | `recognize.py` | Face embedding extraction and comparison |
 | `search.py` | Real-time face matching against reference |
+| `faiss_search.py` | FAISS index build and multi-identity face search |
 | `fairface.py` | FairFace attribute prediction (race, gender, age) |
 | `attribute.py` | Age and gender prediction |
 | `spoofing.py` | Face anti-spoofing detection |
@@ -61,6 +63,11 @@ python tools/emotion.py --source 0
 python tools/gaze.py --source assets/test.jpg
 python tools/gaze.py --source 0
 
+# Head pose estimation
+python tools/headpose.py --source assets/test.jpg
+python tools/headpose.py --source 0
+python tools/headpose.py --source 0 --draw-type axis
+
 # Landmarks
 python tools/landmarks.py --source assets/test.jpg
 python tools/landmarks.py --source 0
@@ -108,7 +115,7 @@ python tools/download_model.py  # downloads all
 | Option | Description |
 |--------|-------------|
 | `--source` | Input source: image/video path or camera ID (0, 1, ...) |
-| `--detector` | Choose detector: `retinaface`, `scrfd`, `yolov5face` |
+| `--detector` | Choose detector: `retinaface`, `scrfd`, `yolov5face`, `yolov8face` |
 | `--threshold` | Visualization confidence threshold (default: varies) |
 | `--save-dir` | Output directory (default: `outputs`) |
 

tools/analyze.py

@@ -27,12 +27,17 @@ from uniface.draw import draw_detections
 from uniface.recognition import ArcFace
 
 
-def draw_face_info(image, face, face_id):
-    """Draw face ID and attributes above bounding box."""
+def draw_face_info(image, face):
+    """Draw face attributes above bounding box."""
     x1, y1, _x2, y2 = map(int, face.bbox)
-    lines = [f'ID: {face_id}', f'Conf: {face.confidence:.2f}']
-    if face.age and face.sex:
+    lines = []
+    if face.age is not None and face.sex is not None:
         lines.append(f'{face.sex}, {face.age}y')
+    if face.emotion is not None:
+        lines.append(face.emotion)
+
+    if not lines:
+        return
 
     for i, line in enumerate(lines):
         y_pos = y1 - 10 - (len(lines) - 1 - i) * 25
@@ -95,13 +100,10 @@ def process_image(analyzer, image_path: str, save_dir: str = 'outputs', show_sim
             status = 'Same' if sim > 0.4 else 'Different'
             print(f'  Face {i + 1} ↔ Face {j + 1}: {sim:.3f} ({status})')
 
-    bboxes = [f.bbox for f in faces]
-    scores = [f.confidence for f in faces]
-    landmarks = [f.landmarks for f in faces]
-    draw_detections(image=image, bboxes=bboxes, scores=scores, landmarks=landmarks, corner_bbox=True)
+    draw_detections(image=image, faces=faces, corner_bbox=True)
 
-    for i, face in enumerate(faces, 1):
-        draw_face_info(image, face, i)
+    for face in faces:
+        draw_face_info(image, face)
 
     os.makedirs(save_dir, exist_ok=True)
     output_path = os.path.join(save_dir, f'{Path(image_path).stem}_analysis.jpg')
@@ -137,13 +139,10 @@ def process_video(analyzer, video_path: str, save_dir: str = 'outputs'):
         frame_count += 1
         faces = analyzer.analyze(frame)
 
-        bboxes = [f.bbox for f in faces]
-        scores = [f.confidence for f in faces]
-        landmarks = [f.landmarks for f in faces]
-        draw_detections(image=frame, bboxes=bboxes, scores=scores, landmarks=landmarks, corner_bbox=True)
+        draw_detections(image=frame, faces=faces, corner_bbox=True)
 
-        for i, face in enumerate(faces, 1):
-            draw_face_info(frame, face, i)
+        for face in faces:
+            draw_face_info(frame, face)
 
         cv2.putText(frame, f'Faces: {len(faces)}', (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
         out.write(frame)
@@ -167,19 +166,16 @@ def run_camera(analyzer, camera_id: int = 0):
 
     while True:
         ret, frame = cap.read()
-        frame = cv2.flip(frame, 1)
         if not ret:
             break
+        frame = cv2.flip(frame, 1)
 
         faces = analyzer.analyze(frame)
 
-        bboxes = [f.bbox for f in faces]
-        scores = [f.confidence for f in faces]
-        landmarks = [f.landmarks for f in faces]
-        draw_detections(image=frame, bboxes=bboxes, scores=scores, landmarks=landmarks, corner_bbox=True)
+        draw_detections(image=frame, faces=faces, corner_bbox=True)
 
-        for i, face in enumerate(faces, 1):
-            draw_face_info(frame, face, i)
+        for face in faces:
+            draw_face_info(frame, face)
 
         cv2.putText(frame, f'Faces: {len(faces)}', (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
         cv2.imshow('Face Analyzer', frame)
@@ -201,7 +197,7 @@ def main():
     detector = RetinaFace()
     recognizer = ArcFace()
     age_gender = AgeGender()
-    analyzer = FaceAnalyzer(detector, recognizer, age_gender)
+    analyzer = FaceAnalyzer(detector, recognizer=recognizer, attributes=[age_gender])
 
     source_type = get_source_type(args.source)
 

tools/anonymize.py

@@ -43,10 +43,7 @@ def process_image(
         from uniface.draw import draw_detections
 
         preview = image.copy()
-        bboxes = [face.bbox for face in faces]
-        scores = [face.confidence for face in faces]
-        landmarks = [face.landmarks for face in faces]
-        draw_detections(preview, bboxes, scores, landmarks)
+        draw_detections(image=preview, faces=faces)
 
         cv2.imshow('Detections (Press any key to continue)', preview)
         cv2.waitKey(0)
@@ -121,9 +118,9 @@ def run_camera(detector, blurrer: BlurFace, camera_id: int = 0):
 
     while True:
         ret, frame = cap.read()
-        frame = cv2.flip(frame, 1)
         if not ret:
             break
+        frame = cv2.flip(frame, 1)
 
         faces = detector.detect(frame)
         if faces:

tools/attribute.py

@@ -52,15 +52,10 @@ def process_image(
     if not faces:
         return
 
-    bboxes = [f.bbox for f in faces]
-    scores = [f.confidence for f in faces]
-    landmarks = [f.landmarks for f in faces]
-    draw_detections(
-        image=image, bboxes=bboxes, scores=scores, landmarks=landmarks, vis_threshold=threshold, corner_bbox=True
-    )
+    draw_detections(image=image, faces=faces, vis_threshold=threshold, corner_bbox=True)
 
     for i, face in enumerate(faces):
-        result = age_gender.predict(image, face.bbox)
+        result = age_gender.predict(image, face)
         print(f'  Face {i + 1}: {result.sex}, {result.age} years old')
         draw_age_gender_label(image, face.bbox, result.sex, result.age)
 
@@ -104,15 +99,10 @@ def process_video(
         frame_count += 1
         faces = detector.detect(frame)
 
-        bboxes = [f.bbox for f in faces]
-        scores = [f.confidence for f in faces]
-        landmarks = [f.landmarks for f in faces]
-        draw_detections(
-            image=frame, bboxes=bboxes, scores=scores, landmarks=landmarks, vis_threshold=threshold, corner_bbox=True
-        )
+        draw_detections(image=frame, faces=faces, vis_threshold=threshold, corner_bbox=True)
 
         for face in faces:
-            result = age_gender.predict(frame, face.bbox)
+            result = age_gender.predict(frame, face)
             draw_age_gender_label(frame, face.bbox, result.sex, result.age)
 
         cv2.putText(frame, f'Faces: {len(faces)}', (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
@@ -137,21 +127,16 @@ def run_camera(detector, age_gender, camera_id: int = 0, threshold: float = 0.6)
 
     while True:
         ret, frame = cap.read()
-        frame = cv2.flip(frame, 1)
         if not ret:
             break
+        frame = cv2.flip(frame, 1)
 
         faces = detector.detect(frame)
 
-        bboxes = [f.bbox for f in faces]
-        scores = [f.confidence for f in faces]
-        landmarks = [f.landmarks for f in faces]
-        draw_detections(
-            image=frame, bboxes=bboxes, scores=scores, landmarks=landmarks, vis_threshold=threshold, corner_bbox=True
-        )
+        draw_detections(image=frame, faces=faces, vis_threshold=threshold, corner_bbox=True)
 
         for face in faces:
-            result = age_gender.predict(frame, face.bbox)
+            result = age_gender.predict(frame, face)
             draw_age_gender_label(frame, face.bbox, result.sex, result.age)
 
         cv2.putText(frame, f'Faces: {len(faces)}', (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)

tools/batch_process.py

@@ -34,13 +34,7 @@ def process_image(detector, image_path: Path, output_path: Path, threshold: floa
 
     faces = detector.detect(image)
 
-    # unpack face data for visualization
-    bboxes = [f.bbox for f in faces]
-    scores = [f.confidence for f in faces]
-    landmarks = [f.landmarks for f in faces]
-    draw_detections(
-        image=image, bboxes=bboxes, scores=scores, landmarks=landmarks, vis_threshold=threshold, corner_bbox=True
-    )
+    draw_detections(image=image, faces=faces, vis_threshold=threshold, corner_bbox=True)
 
     cv2.putText(
         image,

tools/detect.py

@@ -35,10 +35,7 @@ def process_image(detector, image_path: str, threshold: float = 0.6, save_dir: s
     faces = detector.detect(image)
 
     if faces:
-        bboxes = [face.bbox for face in faces]
-        scores = [face.confidence for face in faces]
-        landmarks = [face.landmarks for face in faces]
-        draw_detections(image=image, bboxes=bboxes, scores=scores, landmarks=landmarks, vis_threshold=threshold)
+        draw_detections(image=image, faces=faces, vis_threshold=threshold)
 
     os.makedirs(save_dir, exist_ok=True)
     output_path = os.path.join(save_dir, f'{os.path.splitext(os.path.basename(image_path))[0]}_out.jpg')
@@ -89,14 +86,9 @@ def process_video(
         faces = detector.detect(frame)
         total_faces += len(faces)
 
-        bboxes = [f.bbox for f in faces]
-        scores = [f.confidence for f in faces]
-        landmarks = [f.landmarks for f in faces]
         draw_detections(
             image=frame,
-            bboxes=bboxes,
-            scores=scores,
-            landmarks=landmarks,
+            faces=faces,
             vis_threshold=threshold,
             draw_score=True,
             corner_bbox=True,
@@ -135,20 +127,15 @@ def run_camera(detector, camera_id: int = 0, threshold: float = 0.6):
     prev_time = time.perf_counter()
     while True:
         ret, frame = cap.read()
-        frame = cv2.flip(frame, 1)
         if not ret:
             break
+        frame = cv2.flip(frame, 1)
 
         faces = detector.detect(frame)
 
-        bboxes = [f.bbox for f in faces]
-        scores = [f.confidence for f in faces]
-        landmarks = [f.landmarks for f in faces]
         draw_detections(
             image=frame,
-            bboxes=bboxes,
-            scores=scores,
-            landmarks=landmarks,
+            faces=faces,
             vis_threshold=threshold,
             draw_score=True,
             corner_bbox=True,

tools/download_model.py

@@ -1,9 +1,12 @@
 import argparse
 
 from uniface.constants import (
+    AdaFaceWeights,
     AgeGenderWeights,
     ArcFaceWeights,
     DDAMFNWeights,
+    EdgeFaceWeights,
+    HeadPoseWeights,
     LandmarkWeights,
     MobileFaceWeights,
     RetinaFaceWeights,
@@ -14,13 +17,16 @@ from uniface.model_store import verify_model_weights
 
 MODEL_TYPES = {
     'retinaface': RetinaFaceWeights,
-    'sphereface': SphereFaceWeights,
-    'mobileface': MobileFaceWeights,
+    'adaface': AdaFaceWeights,
     'arcface': ArcFaceWeights,
+    'edgeface': EdgeFaceWeights,
+    'mobileface': MobileFaceWeights,
+    'sphereface': SphereFaceWeights,
     'scrfd': SCRFDWeights,
     'ddamfn': DDAMFNWeights,
     'agegender': AgeGenderWeights,
     'landmark': LandmarkWeights,
+    'headpose': HeadPoseWeights,
 }
 
 

tools/emotion.py

@@ -52,15 +52,10 @@ def process_image(
     if not faces:
         return
 
-    bboxes = [f.bbox for f in faces]
-    scores = [f.confidence for f in faces]
-    landmarks = [f.landmarks for f in faces]
-    draw_detections(
-        image=image, bboxes=bboxes, scores=scores, landmarks=landmarks, vis_threshold=threshold, corner_bbox=True
-    )
+    draw_detections(image=image, faces=faces, vis_threshold=threshold, corner_bbox=True)
 
     for i, face in enumerate(faces):
-        result = emotion_predictor.predict(image, face.landmarks)
+        result = emotion_predictor.predict(image, face)
         print(f'  Face {i + 1}: {result.emotion} (confidence: {result.confidence:.3f})')
         draw_emotion_label(image, face.bbox, result.emotion, result.confidence)
 
@@ -104,15 +99,10 @@ def process_video(
         frame_count += 1
         faces = detector.detect(frame)
 
-        bboxes = [f.bbox for f in faces]
-        scores = [f.confidence for f in faces]
-        landmarks = [f.landmarks for f in faces]
-        draw_detections(
-            image=frame, bboxes=bboxes, scores=scores, landmarks=landmarks, vis_threshold=threshold, corner_bbox=True
-        )
+        draw_detections(image=frame, faces=faces, vis_threshold=threshold, corner_bbox=True)
 
         for face in faces:
-            result = emotion_predictor.predict(frame, face.landmarks)
+            result = emotion_predictor.predict(frame, face)
             draw_emotion_label(frame, face.bbox, result.emotion, result.confidence)
 
         cv2.putText(frame, f'Faces: {len(faces)}', (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
@@ -137,21 +127,16 @@ def run_camera(detector, emotion_predictor, camera_id: int = 0, threshold: float
 
     while True:
         ret, frame = cap.read()
-        frame = cv2.flip(frame, 1)
         if not ret:
             break
+        frame = cv2.flip(frame, 1)
 
         faces = detector.detect(frame)
 
-        bboxes = [f.bbox for f in faces]
-        scores = [f.confidence for f in faces]
-        landmarks = [f.landmarks for f in faces]
-        draw_detections(
-            image=frame, bboxes=bboxes, scores=scores, landmarks=landmarks, vis_threshold=threshold, corner_bbox=True
-        )
+        draw_detections(image=frame, faces=faces, vis_threshold=threshold, corner_bbox=True)
 
         for face in faces:
-            result = emotion_predictor.predict(frame, face.landmarks)
+            result = emotion_predictor.predict(frame, face)
             draw_emotion_label(frame, face.bbox, result.emotion, result.confidence)
 
         cv2.putText(frame, f'Faces: {len(faces)}', (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)

tools/fairface.py

@@ -52,15 +52,10 @@ def process_image(
     if not faces:
         return
 
-    bboxes = [f.bbox for f in faces]
-    scores = [f.confidence for f in faces]
-    landmarks = [f.landmarks for f in faces]
-    draw_detections(
-        image=image, bboxes=bboxes, scores=scores, landmarks=landmarks, vis_threshold=threshold, corner_bbox=True
-    )
+    draw_detections(image=image, faces=faces, vis_threshold=threshold, corner_bbox=True)
 
     for i, face in enumerate(faces):
-        result = fairface.predict(image, face.bbox)
+        result = fairface.predict(image, face)
         print(f'  Face {i + 1}: {result.sex}, {result.age_group}, {result.race}')
         draw_fairface_label(image, face.bbox, result.sex, result.age_group, result.race)
 
@@ -104,15 +99,10 @@ def process_video(
         frame_count += 1
         faces = detector.detect(frame)
 
-        bboxes = [f.bbox for f in faces]
-        scores = [f.confidence for f in faces]
-        landmarks = [f.landmarks for f in faces]
-        draw_detections(
-            image=frame, bboxes=bboxes, scores=scores, landmarks=landmarks, vis_threshold=threshold, corner_bbox=True
-        )
+        draw_detections(image=frame, faces=faces, vis_threshold=threshold, corner_bbox=True)
 
         for face in faces:
-            result = fairface.predict(frame, face.bbox)
+            result = fairface.predict(frame, face)
             draw_fairface_label(frame, face.bbox, result.sex, result.age_group, result.race)
 
         cv2.putText(frame, f'Faces: {len(faces)}', (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
@@ -137,21 +127,16 @@ def run_camera(detector, fairface, camera_id: int = 0, threshold: float = 0.6):
 
     while True:
         ret, frame = cap.read()
-        frame = cv2.flip(frame, 1)
         if not ret:
             break
+        frame = cv2.flip(frame, 1)
 
         faces = detector.detect(frame)
 
-        bboxes = [f.bbox for f in faces]
-        scores = [f.confidence for f in faces]
-        landmarks = [f.landmarks for f in faces]
-        draw_detections(
-            image=frame, bboxes=bboxes, scores=scores, landmarks=landmarks, vis_threshold=threshold, corner_bbox=True
-        )
+        draw_detections(image=frame, faces=faces, vis_threshold=threshold, corner_bbox=True)
 
         for face in faces:
-            result = fairface.predict(frame, face.bbox)
+            result = fairface.predict(frame, face)
             draw_fairface_label(frame, face.bbox, result.sex, result.age_group, result.race)
 
         cv2.putText(frame, f'Faces: {len(faces)}', (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)

tools/faiss_search.py

@@ -24,7 +24,7 @@ import cv2
 
 from uniface import create_detector, create_recognizer
 from uniface.draw import draw_corner_bbox, draw_text_label
-from uniface.indexing import FAISS
+from uniface.stores import FAISS
 
 
 def _draw_face(image, bbox, text: str, color: tuple[int, int, int]) -> None:
@@ -97,9 +97,9 @@ def run_camera(detector, recognizer, store: FAISS, camera_id: int = 0, threshold
 
     while True:
         ret, frame = cap.read()
-        frame = cv2.flip(frame, 1)
         if not ret:
             break
+        frame = cv2.flip(frame, 1)
 
         frame = process_frame(frame, detector, recognizer, store, threshold)
 

tools/headpose.py

@@ -0,0 +1,181 @@
+# Copyright 2025-2026 Yakhyokhuja Valikhujaev
+# Author: Yakhyokhuja Valikhujaev
+# GitHub: https://github.com/yakhyo
+
+"""Head pose estimation on detected faces.
+
+Usage:
+    python tools/headpose.py --source path/to/image.jpg
+    python tools/headpose.py --source path/to/video.mp4
+    python tools/headpose.py --source 0  # webcam
+    python tools/headpose.py --source path/to/image.jpg --draw-type axis
+"""
+
+from __future__ import annotations
+
+import argparse
+import os
+from pathlib import Path
+
+from _common import get_source_type
+import cv2
+
+from uniface.detection import RetinaFace
+from uniface.draw import draw_head_pose
+from uniface.headpose import HeadPose
+
+
+def process_image(detector, head_pose_estimator, image_path: str, save_dir: str = 'outputs', draw_type: str = 'cube'):
+    """Process a single image."""
+    image = cv2.imread(image_path)
+    if image is None:
+        print(f"Error: Failed to load image from '{image_path}'")
+        return
+
+    faces = detector.detect(image)
+    print(f'Detected {len(faces)} face(s)')
+
+    for i, face in enumerate(faces):
+        bbox = face.bbox
+        x1, y1, x2, y2 = map(int, bbox[:4])
+        face_crop = image[y1:y2, x1:x2]
+
+        if face_crop.size == 0:
+            continue
+
+        result = head_pose_estimator.estimate(face_crop)
+        print(f'  Face {i + 1}: pitch={result.pitch:.1f}°, yaw={result.yaw:.1f}°, roll={result.roll:.1f}°')
+
+        draw_head_pose(image, bbox, result.pitch, result.yaw, result.roll, draw_type=draw_type)
+
+    os.makedirs(save_dir, exist_ok=True)
+    output_path = os.path.join(save_dir, f'{Path(image_path).stem}_headpose.jpg')
+    cv2.imwrite(output_path, image)
+    print(f'Output saved: {output_path}')
+
+
+def process_video(detector, head_pose_estimator, video_path: str, save_dir: str = 'outputs', draw_type: str = 'cube'):
+    """Process a video file."""
+    cap = cv2.VideoCapture(video_path)
+    if not cap.isOpened():
+        print(f"Error: Cannot open video file '{video_path}'")
+        return
+
+    fps = cap.get(cv2.CAP_PROP_FPS)
+    width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+    height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+    total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+
+    os.makedirs(save_dir, exist_ok=True)
+    output_path = os.path.join(save_dir, f'{Path(video_path).stem}_headpose.mp4')
+    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
+    out = cv2.VideoWriter(output_path, fourcc, fps, (width, height))
+
+    print(f'Processing video: {video_path} ({total_frames} frames)')
+    frame_count = 0
+
+    while True:
+        ret, frame = cap.read()
+        if not ret:
+            break
+
+        frame_count += 1
+        faces = detector.detect(frame)
+
+        for face in faces:
+            bbox = face.bbox
+            x1, y1, x2, y2 = map(int, bbox[:4])
+            face_crop = frame[y1:y2, x1:x2]
+
+            if face_crop.size == 0:
+                continue
+
+            result = head_pose_estimator.estimate(face_crop)
+            draw_head_pose(frame, bbox, result.pitch, result.yaw, result.roll, draw_type=draw_type)
+
+        cv2.putText(frame, f'Faces: {len(faces)}', (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
+        out.write(frame)
+
+        if frame_count % 100 == 0:
+            print(f'  Processed {frame_count}/{total_frames} frames...')
+
+    cap.release()
+    out.release()
+    print(f'Done! Output saved: {output_path}')
+
+
+def run_camera(detector, head_pose_estimator, camera_id: int = 0, draw_type: str = 'cube'):
+    """Run real-time detection on webcam."""
+    cap = cv2.VideoCapture(camera_id)
+    if not cap.isOpened():
+        print(f'Cannot open camera {camera_id}')
+        return
+
+    print("Press 'q' to quit")
+
+    while True:
+        ret, frame = cap.read()
+        if not ret:
+            break
+
+        frame = cv2.flip(frame, 1)
+        faces = detector.detect(frame)
+
+        for face in faces:
+            bbox = face.bbox
+            x1, y1, x2, y2 = map(int, bbox[:4])
+            face_crop = frame[y1:y2, x1:x2]
+
+            if face_crop.size == 0:
+                continue
+
+            result = head_pose_estimator.estimate(face_crop)
+            draw_head_pose(frame, bbox, result.pitch, result.yaw, result.roll, draw_type=draw_type)
+
+        cv2.putText(frame, f'Faces: {len(faces)}', (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
+        cv2.imshow('Head Pose Estimation', frame)
+
+        if cv2.waitKey(1) & 0xFF == ord('q'):
+            break
+
+    cap.release()
+    cv2.destroyAllWindows()
+
+
+def main():
+    parser = argparse.ArgumentParser(description='Run head pose estimation')
+    parser.add_argument('--source', type=str, required=True, help='Image/video path or camera ID (0, 1, ...)')
+    parser.add_argument('--save-dir', type=str, default='outputs', help='Output directory')
+    parser.add_argument(
+        '--draw-type',
+        type=str,
+        default='cube',
+        choices=['cube', 'axis'],
+        help='Visualization type: cube (default) or axis',
+    )
+    args = parser.parse_args()
+
+    detector = RetinaFace()
+    head_pose_estimator = HeadPose()
+
+    source_type = get_source_type(args.source)
+
+    if source_type == 'camera':
+        run_camera(detector, head_pose_estimator, int(args.source), args.draw_type)
+    elif source_type == 'image':
+        if not os.path.exists(args.source):
+            print(f'Error: Image not found: {args.source}')
+            return
+        process_image(detector, head_pose_estimator, args.source, args.save_dir, args.draw_type)
+    elif source_type == 'video':
+        if not os.path.exists(args.source):
+            print(f'Error: Video not found: {args.source}')
+            return
+        process_video(detector, head_pose_estimator, args.source, args.save_dir, args.draw_type)
+    else:
+        print(f"Error: Unknown source type for '{args.source}'")
+        print('Supported formats: images (.jpg, .png, ...), videos (.mp4, .avi, ...), or camera ID (0, 1, ...)')
+
+
+if __name__ == '__main__':
+    main()

tools/landmarks.py

@@ -114,9 +114,9 @@ def run_camera(detector, landmarker, camera_id: int = 0):
 
     while True:
         ret, frame = cap.read()
-        frame = cv2.flip(frame, 1)
         if not ret:
             break
+        frame = cv2.flip(frame, 1)
 
         faces = detector.detect(frame)
 

tools/recognize.py

@@ -16,16 +16,22 @@ import numpy as np
 
 from uniface.detection import SCRFD, RetinaFace
 from uniface.face_utils import compute_similarity
-from uniface.recognition import ArcFace, MobileFace, SphereFace
+from uniface.recognition import AdaFace, ArcFace, EdgeFace, MobileFace, SphereFace
+
+RECOGNIZERS = {
+    'arcface': ArcFace,
+    'adaface': AdaFace,
+    'edgeface': EdgeFace,
+    'mobileface': MobileFace,
+    'sphereface': SphereFace,
+}
 
 
 def get_recognizer(name: str):
-    if name == 'arcface':
-        return ArcFace()
-    elif name == 'mobileface':
-        return MobileFace()
-    else:
-        return SphereFace()
+    cls = RECOGNIZERS.get(name)
+    if cls is None:
+        raise ValueError(f"Unknown recognizer: '{name}'. Available: {list(RECOGNIZERS)}")
+    return cls()
 
 
 def run_inference(detector, recognizer, image_path: str):
@@ -41,12 +47,13 @@ def run_inference(detector, recognizer, image_path: str):
 
     print(f'Detected {len(faces)} face(s). Extracting embedding for the first face...')
 
-    landmarks = faces[0].landmarks  # 5-point landmarks for alignment (already np.ndarray)
+    landmarks = faces[0].landmarks
     embedding = recognizer.get_embedding(image, landmarks)
-    norm_embedding = recognizer.get_normalized_embedding(image, landmarks)  # L2 normalized
+    raw_norm = np.linalg.norm(embedding)
+    norm_embedding = embedding.ravel() / raw_norm if raw_norm > 0 else embedding.ravel()
 
     print(f'  Embedding shape: {embedding.shape}')
-    print(f'  L2 norm (raw): {np.linalg.norm(embedding):.4f}')
+    print(f'  L2 norm (raw): {raw_norm:.4f}')
     print(f'  L2 norm (normalized): {np.linalg.norm(norm_embedding):.4f}')
 
 
@@ -90,7 +97,7 @@ def main():
         '--recognizer',
         type=str,
         default='arcface',
-        choices=['arcface', 'mobileface', 'sphereface'],
+        choices=list(RECOGNIZERS),
     )
     args = parser.parse_args()
 

tools/search.py

@@ -109,9 +109,9 @@ def run_camera(detector, recognizer, ref_embedding: np.ndarray, camera_id: int =
 
     while True:
         ret, frame = cap.read()
-        frame = cv2.flip(frame, 1)
         if not ret:
             break
+        frame = cv2.flip(frame, 1)
 
         frame = process_frame(frame, detector, recognizer, ref_embedding, threshold)
 

tools/track.py

@@ -134,9 +134,9 @@ def run_camera(
 
     while True:
         ret, frame = cap.read()
-        frame = cv2.flip(frame, 1)
         if not ret:
             break
+        frame = cv2.flip(frame, 1)
 
         # Detect faces
         faces = detector.detect(frame)

uniface/__init__.py

@@ -15,11 +15,13 @@
 
 This library provides unified APIs for:
 - Face detection (RetinaFace, SCRFD, YOLOv5Face, YOLOv8Face)
-- Face recognition (AdaFace, ArcFace, MobileFace, SphereFace)
+- Face recognition (AdaFace, ArcFace, EdgeFace, MobileFace, SphereFace)
 - Face tracking (ByteTrack with Kalman filtering)
 - Facial landmarks (106-point detection)
 - Face parsing (semantic segmentation)
+- Portrait matting (trimap-free alpha matte)
 - Gaze estimation
+- Head pose estimation
 - Age, gender, and emotion prediction
 - Face anti-spoofing
 - Privacy/anonymization
@@ -29,7 +31,7 @@ from __future__ import annotations
 
 __license__ = 'MIT'
 __author__ = 'Yakhyokhuja Valikhujaev'
-__version__ = '3.1.0'
+__version__ = '3.5.3'
 
 import contextlib
 
@@ -38,7 +40,7 @@ from uniface.log import Logger, enable_logging
 from uniface.model_store import download_models, get_cache_dir, set_cache_dir, verify_model_weights
 
 from .analyzer import FaceAnalyzer
-from .attribute import AgeGender, Emotion, FairFace
+from .attribute import AgeGender, Emotion, FairFace, create_attribute_predictor
 from .detection import (
     SCRFD,
     RetinaFace,
@@ -48,17 +50,19 @@ from .detection import (
     list_available_detectors,
 )
 from .gaze import MobileGaze, create_gaze_estimator
+from .headpose import HeadPose, create_head_pose_estimator
 from .landmark import Landmark106, create_landmarker
+from .matting import MODNet, create_matting_model
 from .parsing import BiSeNet, XSeg, create_face_parser
 from .privacy import BlurFace
-from .recognition import AdaFace, ArcFace, MobileFace, SphereFace, create_recognizer
+from .recognition import AdaFace, ArcFace, EdgeFace, MobileFace, SphereFace, create_recognizer
 from .spoofing import MiniFASNet, create_spoofer
 from .tracking import BYTETracker
-from .types import AttributeResult, EmotionResult, Face, GazeResult, SpoofingResult
+from .types import AttributeResult, EmotionResult, Face, GazeResult, HeadPoseResult, SpoofingResult
 
 # Optional: FAISS vector store (requires `pip install faiss-cpu`)
 with contextlib.suppress(ImportError):
-    from .indexing import FAISS
+    from .stores import FAISS
 
 __all__ = [
     # Metadata
@@ -72,6 +76,8 @@ __all__ = [
     'create_detector',
     'create_face_parser',
     'create_gaze_estimator',
+    'create_matting_model',
+    'create_head_pose_estimator',
     'create_landmarker',
     'create_recognizer',
     'create_spoofer',
@@ -84,6 +90,7 @@ __all__ = [
     # Recognition models
     'AdaFace',
     'ArcFace',
+    'EdgeFace',
     'MobileFace',
     'SphereFace',
     # Landmark models
@@ -91,12 +98,18 @@ __all__ = [
     # Gaze models
     'GazeResult',
     'MobileGaze',
+    # Head pose models
+    'HeadPose',
+    'HeadPoseResult',
+    # Matting models
+    'MODNet',
     # Parsing models
     'BiSeNet',
     'XSeg',
     # Attribute models
     'AgeGender',
     'AttributeResult',
+    'create_attribute_predictor',
     'Emotion',
     'EmotionResult',
     'FairFace',
@@ -107,7 +120,7 @@ __all__ = [
     'BYTETracker',
     # Privacy
     'BlurFace',
-    # Indexing (optional)
+    # Stores (optional)
     'FAISS',
     # Utilities
     'Logger',

uniface/analyzer.py

@@ -4,10 +4,11 @@
 
 from __future__ import annotations
 
+from typing import Any
+
 import numpy as np
 
-from uniface.attribute.age_gender import AgeGender
-from uniface.attribute.fairface import FairFace
+from uniface.attribute.base import Attribute
 from uniface.detection.base import BaseDetector
 from uniface.log import Logger
 from uniface.recognition.base import BaseRecognizer
@@ -15,53 +16,73 @@ from uniface.types import Face
 
 __all__ = ['FaceAnalyzer']
 
+_UNSET: Any = object()
+
 
 class FaceAnalyzer:
     """Unified face analyzer combining detection, recognition, and attributes.
 
     This class provides a high-level interface for face analysis by combining
     multiple components: face detection, recognition (embedding extraction),
-    and attribute prediction (age, gender, race).
+    and an extensible list of attribute predictors (age, gender, race,
+    emotion, etc.).
+
+    Any :class:`~uniface.attribute.base.Attribute` subclass can be passed
+    via the ``attributes`` list.  Each predictor's ``predict(image, face)``
+    is called once per detected face, enriching the :class:`Face` in-place.
+
+    When called with no arguments, uses SCRFD (500M) for detection and
+    ArcFace (MobileNet) for recognition — the smallest and fastest variants.
 
     Args:
-        detector: Face detector instance for detecting faces in images.
-        recognizer: Optional face recognizer for extracting embeddings.
-        age_gender: Optional age/gender predictor.
-        fairface: Optional FairFace predictor for demographics.
+        detector: Face detector instance. Defaults to ``SCRFD(SCRFD_500M_KPS)``.
+        recognizer: Face recognizer for extracting embeddings.
+            Defaults to ``ArcFace(MNET)``. Pass ``None`` to disable recognition.
+        attributes: Optional list of ``Attribute`` predictors to run on
+            each detected face (e.g. ``[AgeGender()]``).
 
-    Example:
-        >>> from uniface import RetinaFace, ArcFace, FaceAnalyzer
-        >>> detector = RetinaFace()
-        >>> recognizer = ArcFace()
-        >>> analyzer = FaceAnalyzer(detector, recognizer=recognizer)
+    Examples:
+        >>> from uniface import FaceAnalyzer
+        >>> analyzer = FaceAnalyzer()
+        >>> faces = analyzer.analyze(image)
+
+        >>> from uniface import FaceAnalyzer, AgeGender
+        >>> analyzer = FaceAnalyzer(attributes=[AgeGender()])
         >>> faces = analyzer.analyze(image)
     """
 
     def __init__(
         self,
-        detector: BaseDetector,
-        recognizer: BaseRecognizer | None = None,
-        age_gender: AgeGender | None = None,
-        fairface: FairFace | None = None,
+        detector: BaseDetector | None = None,
+        recognizer: BaseRecognizer | None = _UNSET,
+        attributes: list[Attribute] | None = None,
     ) -> None:
+        if detector is None:
+            from uniface.constants import SCRFDWeights
+            from uniface.detection import SCRFD
+
+            detector = SCRFD(model_name=SCRFDWeights.SCRFD_500M_KPS)
+
+        if recognizer is _UNSET:
+            from uniface.recognition import ArcFace
+
+            recognizer = ArcFace()
+
         self.detector = detector
         self.recognizer = recognizer
-        self.age_gender = age_gender
-        self.fairface = fairface
+        self.attributes: list[Attribute] = attributes or []
 
         Logger.info(f'Initialized FaceAnalyzer with detector={detector.__class__.__name__}')
         if recognizer:
-            Logger.info(f'  - Recognition enabled: {recognizer.__class__.__name__}')
-        if age_gender:
-            Logger.info(f'  - Age/Gender enabled: {age_gender.__class__.__name__}')
-        if fairface:
-            Logger.info(f'  - FairFace enabled: {fairface.__class__.__name__}')
+            Logger.info(f'Recognition enabled: {recognizer.__class__.__name__}')
+        for attr in self.attributes:
+            Logger.info(f'Attribute enabled: {attr.__class__.__name__}')
 
     def analyze(self, image: np.ndarray) -> list[Face]:
         """Analyze faces in an image.
 
-        Performs face detection and optionally extracts embeddings and
-        predicts attributes for each detected face.
+        Performs face detection, optionally extracts embeddings, and runs
+        every registered attribute predictor on each detected face.
 
         Args:
             image: Input image as numpy array with shape (H, W, C) in BGR format.
@@ -76,28 +97,17 @@ class FaceAnalyzer:
             if self.recognizer is not None:
                 try:
                     face.embedding = self.recognizer.get_normalized_embedding(image, face.landmarks)
-                    Logger.debug(f'  Face {idx + 1}: Extracted embedding with shape {face.embedding.shape}')
+                    Logger.debug(f'Face {idx + 1}: Extracted embedding with shape {face.embedding.shape}')
                 except Exception as e:
-                    Logger.warning(f'  Face {idx + 1}: Failed to extract embedding: {e}')
+                    Logger.warning(f'Face {idx + 1}: Failed to extract embedding: {e}')
 
-            if self.age_gender is not None:
+            for attr in self.attributes:
+                attr_name = attr.__class__.__name__
                 try:
-                    result = self.age_gender.predict(image, face.bbox)
-                    face.gender = result.gender
-                    face.age = result.age
-                    Logger.debug(f'  Face {idx + 1}: Age={face.age}, Gender={face.sex}')
+                    attr.predict(image, face)
+                    Logger.debug(f'Face {idx + 1}: {attr_name} prediction succeeded')
                 except Exception as e:
-                    Logger.warning(f'  Face {idx + 1}: Failed to predict age/gender: {e}')
-
-            if self.fairface is not None:
-                try:
-                    result = self.fairface.predict(image, face.bbox)
-                    face.gender = result.gender
-                    face.age_group = result.age_group
-                    face.race = result.race
-                    Logger.debug(f'  Face {idx + 1}: AgeGroup={face.age_group}, Gender={face.sex}, Race={face.race}')
-                except Exception as e:
-                    Logger.warning(f'  Face {idx + 1}: Failed to predict FairFace attributes: {e}')
+                    Logger.warning(f'Face {idx + 1}: {attr_name} prediction failed: {e}')
 
         Logger.info(f'Analysis complete: {len(faces)} face(s) processed')
         return faces
@@ -106,8 +116,6 @@ class FaceAnalyzer:
         parts = [f'FaceAnalyzer(detector={self.detector.__class__.__name__}']
         if self.recognizer:
             parts.append(f'recognizer={self.recognizer.__class__.__name__}')
-        if self.age_gender:
-            parts.append(f'age_gender={self.age_gender.__class__.__name__}')
-        if self.fairface:
-            parts.append(f'fairface={self.fairface.__class__.__name__}')
+        for attr in self.attributes:
+            parts.append(f'{attr.__class__.__name__}')
         return ', '.join(parts) + ')'

uniface/attribute/__init__.py

@@ -12,7 +12,7 @@ from uniface.attribute.age_gender import AgeGender
 from uniface.attribute.base import Attribute
 from uniface.attribute.fairface import FairFace
 from uniface.constants import AgeGenderWeights, DDAMFNWeights, FairFaceWeights
-from uniface.types import AttributeResult, EmotionResult
+from uniface.types import AttributeResult, EmotionResult, Face
 
 try:
     from uniface.attribute.emotion import Emotion
@@ -30,7 +30,7 @@ except ImportError:
         def _initialize_model(self) -> None: ...
         def preprocess(self, image: np.ndarray, *args: Any) -> Any: ...
         def postprocess(self, prediction: Any) -> Any: ...
-        def predict(self, image: np.ndarray, *args: Any) -> Any: ...
+        def predict(self, image: np.ndarray, face: Face) -> Any: ...
 
 
 __all__ = [

uniface/attribute/age_gender.py

@@ -12,7 +12,7 @@ from uniface.face_utils import bbox_center_alignment
 from uniface.log import Logger
 from uniface.model_store import verify_model_weights
 from uniface.onnx_utils import create_onnx_session
-from uniface.types import AttributeResult
+from uniface.types import AttributeResult, Face
 
 __all__ = ['AgeGender']
 
@@ -133,17 +133,20 @@ class AgeGender(Attribute):
         age = int(np.round(prediction[2] * 100))
         return AttributeResult(gender=gender, age=age)
 
-    def predict(self, image: np.ndarray, bbox: list | np.ndarray) -> AttributeResult:
-        """
-        Predicts age and gender for a single face specified by a bounding box.
+    def predict(self, image: np.ndarray, face: Face) -> AttributeResult:
+        """Predict age and gender and enrich the Face in-place.
 
         Args:
-            image (np.ndarray): The full input image in BGR format.
-            bbox (Union[List, np.ndarray]): The face bounding box coordinates [x1, y1, x2, y2].
+            image: The full input image in BGR format.
+            face: Detected face; ``face.bbox`` is used for alignment.
 
         Returns:
-            AttributeResult: Result containing gender (0=Female, 1=Male) and age (in years).
+            ``AttributeResult`` with gender (0=Female, 1=Male) and age (years).
         """
-        face_blob = self.preprocess(image, bbox)
+        face_blob = self.preprocess(image, face.bbox)
         prediction = self.session.run(self.output_names, {self.input_name: face_blob})[0][0]
-        return self.postprocess(prediction)
+        result = self.postprocess(prediction)
+
+        face.gender = result.gender
+        face.age = result.age
+        return result

uniface/attribute/base.py

@@ -2,95 +2,78 @@
 # Author: Yakhyokhuja Valikhujaev
 # GitHub: https://github.com/yakhyo
 
+from __future__ import annotations
+
 from abc import ABC, abstractmethod
 from typing import Any
 
 import numpy as np
 
-from uniface.types import AttributeResult, EmotionResult
+from uniface.types import AttributeResult, EmotionResult, Face
 
 __all__ = ['Attribute', 'AttributeResult', 'EmotionResult']
 
 
 class Attribute(ABC):
-    """
-    Abstract base class for face attribute models.
+    """Abstract base class for face attribute models.
 
-    This class defines the common interface that all attribute models
-    (e.g., age-gender, emotion) must implement. It ensures a consistent API
-    across different attribute prediction modules in the library, making them
-    interchangeable and easy to use.
+    All attribute models (age-gender, emotion, FairFace, etc.) implement this
+    interface so they can be used interchangeably inside ``FaceAnalyzer``.
+
+    The ``predict`` method accepts an image and a :class:`Face` object.  Each
+    subclass extracts what it needs (bbox, landmarks) from the Face, runs
+    inference, writes the results back to the Face **and** returns a typed
+    result dataclass.
     """
 
     @abstractmethod
     def _initialize_model(self) -> None:
-        """
-        Initializes the underlying model for inference.
-
-        This method should handle loading model weights, creating the
-        inference session (e.g., ONNX Runtime, PyTorch), and any necessary
-        warm-up procedures to prepare the model for prediction.
-        """
+        """Load model weights and create the inference session."""
         raise NotImplementedError('Subclasses must implement the _initialize_model method.')
 
     @abstractmethod
     def preprocess(self, image: np.ndarray, *args: Any) -> Any:
-        """
-        Preprocesses the input data for the model.
-
-        This method should take a raw image and any other necessary data
-        (like bounding boxes or landmarks) and convert it into the format
-        expected by the model's inference engine (e.g., a blob or tensor).
+        """Preprocess the input data for the model.
 
         Args:
-            image (np.ndarray): The input image containing the face, typically
-                                in BGR format.
-            *args: Additional arguments required for preprocessing, such as
-                   bounding boxes or facial landmarks.
+            image: The input image in BGR format.
+            *args: Subclass-specific data (bbox, landmarks, etc.).
 
         Returns:
-            The preprocessed data ready for model inference.
+            Preprocessed data ready for model inference.
         """
         raise NotImplementedError('Subclasses must implement the preprocess method.')
 
     @abstractmethod
     def postprocess(self, prediction: Any) -> Any:
-        """
-        Postprocesses the raw model output into a human-readable format.
-
-        This method takes the raw output from the model's inference and
-        converts it into a meaningful result, such as an age value, a gender
-        label, or an emotion category.
+        """Convert raw model output into a typed result dataclass.
 
         Args:
-            prediction (Any): The raw output from the model's inference.
+            prediction: Raw output from the model.
 
         Returns:
-            The final, processed attributes.
+            An ``AttributeResult`` or ``EmotionResult``.
         """
         raise NotImplementedError('Subclasses must implement the postprocess method.')
 
     @abstractmethod
-    def predict(self, image: np.ndarray, *args: Any) -> Any:
-        """
-        Performs end-to-end attribute prediction on a given image.
+    def predict(self, image: np.ndarray, face: Face) -> AttributeResult | EmotionResult:
+        """Run end-to-end prediction and enrich the Face in-place.
 
-        This method orchestrates the full pipeline: it calls the preprocess,
-        inference, and postprocess steps to return the final, user-friendly
-        attribute prediction.
+        Each subclass extracts what it needs from *face* (e.g. ``face.bbox``
+        or ``face.landmarks``), runs the full preprocess-infer-postprocess
+        pipeline, writes relevant fields back to *face*, and returns the
+        result dataclass.
 
         Args:
-            image (np.ndarray): The input image containing the face.
-            *args: Additional data required for prediction, such as a bounding
-                   box or landmarks.
+            image: The full input image in BGR format.
+            face: Detected face whose attribute fields will be populated.
 
         Returns:
-            The final predicted attributes.
+            The prediction result (``AttributeResult`` or ``EmotionResult``).
         """
         raise NotImplementedError('Subclasses must implement the predict method.')
 
-    def __call__(self, *args, **kwargs) -> Any:
-        """
-        Provides a convenient, callable shortcut for the `predict` method.
-        """
-        return self.predict(*args, **kwargs)
+    def __call__(self, image: np.ndarray, face: Face) -> AttributeResult | EmotionResult:
+        """Callable shortcut for :meth:`predict`."""
+        return self.predict(image, face)

uniface/attribute/emotion.py

@@ -12,7 +12,7 @@ from uniface.constants import DDAMFNWeights
 from uniface.face_utils import face_alignment
 from uniface.log import Logger
 from uniface.model_store import verify_model_weights
-from uniface.types import EmotionResult
+from uniface.types import EmotionResult, Face
 
 __all__ = ['Emotion']
 
@@ -116,14 +116,23 @@ class Emotion(Attribute):
         confidence = float(probabilities[pred_index])
         return EmotionResult(emotion=emotion_label, confidence=confidence)
 
-    def predict(self, image: np.ndarray, landmark: list | np.ndarray) -> EmotionResult:
+    def predict(self, image: np.ndarray, face: Face) -> EmotionResult:
+        """Predict emotion and enrich the Face in-place.
+
+        Args:
+            image: The full input image in BGR format.
+            face: Detected face; ``face.landmarks`` is used for alignment.
+
+        Returns:
+            ``EmotionResult`` with emotion label and confidence score.
         """
-        Predicts the emotion from a single face specified by its landmarks.
-        """
-        input_tensor = self.preprocess(image, landmark)
+        input_tensor = self.preprocess(image, face.landmarks)
         with torch.no_grad():
             output = self.model(input_tensor)
             if isinstance(output, tuple):
                 output = output[0]
 
-        return self.postprocess(output)
+        result = self.postprocess(output)
+        face.emotion = result.emotion
+        face.emotion_confidence = result.confidence
+        return result

uniface/attribute/fairface.py

@@ -11,7 +11,7 @@ from uniface.constants import FairFaceWeights
 from uniface.log import Logger
 from uniface.model_store import verify_model_weights
 from uniface.onnx_utils import create_onnx_session
-from uniface.types import AttributeResult
+from uniface.types import AttributeResult, Face
 
 __all__ = ['AGE_LABELS', 'RACE_LABELS', 'FairFace']
 
@@ -168,29 +168,24 @@ class FairFace(Attribute):
             race=RACE_LABELS[race_idx],
         )
 
-    def predict(self, image: np.ndarray, bbox: list | np.ndarray | None = None) -> AttributeResult:
-        """
-        Predicts race, gender, and age for a face.
+    def predict(self, image: np.ndarray, face: Face) -> AttributeResult:
+        """Predict race, gender, and age and enrich the Face in-place.
 
         Args:
-            image (np.ndarray): The input image in BGR format.
-            bbox (Optional[Union[List, np.ndarray]]): Face bounding box [x1, y1, x2, y2].
-                If None, uses the entire image.
+            image: The full input image in BGR format.
+            face: Detected face; ``face.bbox`` is used for cropping.
 
         Returns:
-            AttributeResult: Result containing:
-                - gender: 0=Female, 1=Male
-                - age_group: Age range string like "20-29"
-                - race: Race/ethnicity label
+            ``AttributeResult`` with gender, age_group, and race.
         """
-        # Preprocess
-        input_blob = self.preprocess(image, bbox)
-
-        # Inference
+        input_blob = self.preprocess(image, face.bbox)
         outputs = self.session.run(self.output_names, {self.input_name: input_blob})
+        result = self.postprocess(outputs)
 
-        # Postprocess
-        return self.postprocess(outputs)
+        face.gender = result.gender
+        face.age_group = result.age_group
+        face.race = result.race
+        return result
 
     @staticmethod
     def _softmax(x: np.ndarray) -> np.ndarray:

uniface/common.py

@@ -16,6 +16,7 @@ __all__ = [
     'distance2bbox',
     'distance2kps',
     'generate_anchors',
+    'letterbox_resize',
     'non_max_suppression',
     'resize_image',
     'xyxy_to_cxcywh',
@@ -277,3 +278,70 @@ def distance2kps(
         preds.append(px)
         preds.append(py)
     return np.stack(preds, axis=-1)
+
+
+def letterbox_resize(
+    image: np.ndarray,
+    target_size: int,
+    fill_value: int = 114,
+) -> tuple[np.ndarray, float, tuple[int, int]]:
+    """Letterbox resize with center padding for YOLO-style detectors.
+
+    Maintains aspect ratio by scaling the image to fit within target_size,
+    then center-pads with a constant fill value. Converts BGR to RGB,
+    normalizes to [0, 1], and transposes to NCHW format.
+
+    This preprocessing strategy is standard for YOLO models and ensures
+    no distortion while maintaining a square input size.
+
+    Args:
+        image: Input image in BGR format with shape (H, W, C).
+        target_size: Target square size (e.g., 640 for 640x640 input).
+        fill_value: Padding fill value (default: 114 for gray background).
+
+    Returns:
+        Tuple of (preprocessed_tensor, scale_ratio, padding):
+            - preprocessed_tensor: Shape (1, 3, target_size, target_size),
+              RGB, normalized [0, 1], NCHW format, float32, contiguous.
+            - scale_ratio: Resize scale factor for coordinate transformation.
+            - padding: Padding offsets as (pad_w, pad_h) for coordinate transformation.
+
+    Example:
+        >>> image = cv2.imread('face.jpg')  # (480, 640, 3)
+        >>> tensor, scale, (pad_w, pad_h) = letterbox_resize(image, 640)
+        >>> tensor.shape
+        (1, 3, 640, 640)
+        >>> # To transform coordinates back to original:
+        >>> x_orig = (x_detected - pad_w) / scale
+        >>> y_orig = (y_detected - pad_h) / scale
+    """
+    # Get original image shape
+    img_h, img_w = image.shape[:2]
+
+    # Calculate scale ratio to fit within target_size
+    scale = min(target_size / img_h, target_size / img_w)
+    new_h, new_w = int(img_h * scale), int(img_w * scale)
+
+    # Resize image maintaining aspect ratio
+    img_resized = cv2.resize(image, (new_w, new_h), interpolation=cv2.INTER_LINEAR)
+
+    # Create padded canvas with fill_value
+    img_padded = np.full((target_size, target_size, 3), fill_value, dtype=np.uint8)
+
+    # Calculate padding to center the image
+    pad_h = (target_size - new_h) // 2
+    pad_w = (target_size - new_w) // 2
+
+    # Place resized image in center of canvas
+    img_padded[pad_h : pad_h + new_h, pad_w : pad_w + new_w] = img_resized
+
+    # Convert BGR to RGB and normalize to [0, 1]
+    img_rgb = cv2.cvtColor(img_padded, cv2.COLOR_BGR2RGB)
+    img_normalized = img_rgb.astype(np.float32) / 255.0
+
+    # Transpose to CHW format and add batch dimension (NCHW)
+    img_transposed = np.transpose(img_normalized, (2, 0, 1))
+    img_batch = np.expand_dims(img_transposed, axis=0)
+    img_batch = np.ascontiguousarray(img_batch)
+
+    return img_batch, scale, (pad_w, pad_h)

uniface/constants.py

@@ -57,6 +57,18 @@ class AdaFaceWeights(str, Enum):
     IR_18  = "adaface_ir_18"
     IR_101 = "adaface_ir_101"
 
+class EdgeFaceWeights(str, Enum):
+    """
+    EdgeFace: Efficient Face Recognition Model for Edge Devices.
+    Based on EdgeNeXt backbone with optional LoRA low-rank compression.
+    All models output 512-D embeddings from 112x112 aligned face crops.
+    https://github.com/yakhyo/edgeface-onnx
+    """
+    XXS         = "edgeface_xxs"
+    XS_GAMMA_06 = "edgeface_xs_gamma_06"
+    S_GAMMA_05  = "edgeface_s_gamma_05"
+    BASE        = "edgeface_base"
+
 class RetinaFaceWeights(str, Enum):
     """
     Trained on WIDER FACE dataset.
@@ -156,6 +168,20 @@ class GazeWeights(str, Enum):
     MOBILEONE_S0 = "gaze_mobileone_s0"
 
 
+class HeadPoseWeights(str, Enum):
+    """
+    Head pose estimation models using 6D rotation representation.
+    Trained on 300W-LP dataset, evaluated on AFLW2000.
+    https://github.com/yakhyo/head-pose-estimation
+    """
+    RESNET18           = "headpose_resnet18"
+    RESNET34           = "headpose_resnet34"
+    RESNET50           = "headpose_resnet50"
+    MOBILENET_V2       = "headpose_mobilenetv2"
+    MOBILENET_V3_SMALL = "headpose_mobilenetv3_small"
+    MOBILENET_V3_LARGE = "headpose_mobilenetv3_large"
+
+
 class ParsingWeights(str, Enum):
     """
     Face Parsing: Semantic Segmentation of Facial Components.
@@ -175,6 +201,15 @@ class XSegWeights(str, Enum):
     DEFAULT = "xseg"
 
 
+class MODNetWeights(str, Enum):
+    """
+    MODNet: Real-Time Trimap-Free Portrait Matting via Objective Decomposition.
+    https://github.com/yakhyo/modnet
+    """
+    PHOTOGRAPHIC = "modnet_photographic"
+    WEBCAM       = "modnet_webcam"
+
+
 class MiniFASNetWeights(str, Enum):
     """
     MiniFASNet: Lightweight Face Anti-Spoofing models.
@@ -264,6 +299,24 @@ MODEL_REGISTRY: dict[Enum, ModelInfo] = {
         sha256='f2eb07d03de0af560a82e1214df799fec5e09375d43521e2868f9dc387e5a43e'
     ),
 
+    # EdgeFace
+    EdgeFaceWeights.XXS: ModelInfo(
+        url='https://github.com/yakhyo/edgeface-onnx/releases/download/weights/edgeface_xxs.onnx',
+        sha256='dc674de4cbc77fa0bf9a82d5149558ab8581d82a2cd3bb60f28fd1a5d3ff8a2f'
+    ),
+    EdgeFaceWeights.XS_GAMMA_06: ModelInfo(
+        url='https://github.com/yakhyo/edgeface-onnx/releases/download/weights/edgeface_xs_gamma_06.onnx',
+        sha256='9206e2eb13a2761d7b5b76e13016d4b9acd3fa3535a9a09939f3adacd139a5ff'
+    ),
+    EdgeFaceWeights.S_GAMMA_05: ModelInfo(
+        url='https://github.com/yakhyo/edgeface-onnx/releases/download/weights/edgeface_s_gamma_05.onnx',
+        sha256='b850767cf791bda585600b5c4c7d7432b2f998ccd862caae34ef1afa967d2e54'
+    ),
+    EdgeFaceWeights.BASE: ModelInfo(
+        url='https://github.com/yakhyo/edgeface-onnx/releases/download/weights/edgeface_base.onnx',
+        sha256='b56942f072c67385f44734b9458b0ccc4a2226888a113f77e0c802ad0c77b4c3'
+    ),
+
     # SCRFD
     SCRFDWeights.SCRFD_10G_KPS: ModelInfo(
         url='https://github.com/yakhyo/uniface/releases/download/weights/scrfd_10g_kps.onnx',
@@ -348,6 +401,32 @@ MODEL_REGISTRY: dict[Enum, ModelInfo] = {
         sha256='8b4fdc4e3da44733c9a82e7776b411e4a39f94e8e285aee0fc85a548a55f7d9f'
     ),
 
+    # Head Pose
+    HeadPoseWeights.RESNET18: ModelInfo(
+        url='https://github.com/yakhyo/head-pose-estimation/releases/download/weights/resnet18.onnx',
+        sha256='61c34e877989412980d1ea80c52391250b074abc00d19a6100de5c8e999212ee'
+    ),
+    HeadPoseWeights.RESNET34: ModelInfo(
+        url='https://github.com/yakhyo/head-pose-estimation/releases/download/weights/resnet34.onnx',
+        sha256='8da9f2ce4810298ebea68bd85fba1b6bd11716060c10534596f46be52cc908c9'
+    ),
+    HeadPoseWeights.RESNET50: ModelInfo(
+        url='https://github.com/yakhyo/head-pose-estimation/releases/download/weights/resnet50.onnx',
+        sha256='50c74d57b7663361b8ede83b0e4122546171119ef502ec55b790dbd7fc360260'
+    ),
+    HeadPoseWeights.MOBILENET_V2: ModelInfo(
+        url='https://github.com/yakhyo/head-pose-estimation/releases/download/weights/mobilenetv2.onnx',
+        sha256='1e902872868e483bd0e4f8f4a8ff2a4d61c2ccbca9dadf748e5479b5cc86a9e9'
+    ),
+    HeadPoseWeights.MOBILENET_V3_SMALL: ModelInfo(
+        url='https://github.com/yakhyo/head-pose-estimation/releases/download/weights/mobilenetv3_small.onnx',
+        sha256='e8ae4d932b3d13221638fc72e171603e020c6da28b770753f76146867f40e190'
+    ),
+    HeadPoseWeights.MOBILENET_V3_LARGE: ModelInfo(
+        url='https://github.com/yakhyo/head-pose-estimation/releases/download/weights/mobilenetv3_large.onnx',
+        sha256='3a68815fa00aba41ddc4e014bf631b637caba8619df71160383f1fee8c15a3c9'
+    ),
+
     # Parsing
     ParsingWeights.RESNET18: ModelInfo(
         url='https://github.com/yakhyo/face-parsing/releases/download/weights/resnet18.onnx',
@@ -373,6 +452,16 @@ MODEL_REGISTRY: dict[Enum, ModelInfo] = {
         url='https://github.com/yakhyo/face-segmentation/releases/download/weights/xseg.onnx',
         sha256='0b57328efcb839d85973164b617ceee9dfe6cfcb2c82e8a033bba9f4f09b27e5'
     ),
+
+    # MODNet (Portrait Matting)
+    MODNetWeights.PHOTOGRAPHIC: ModelInfo(
+        url='https://github.com/yakhyo/modnet/releases/download/weights/modnet_photographic.onnx',
+        sha256='5069a5e306b9f5e9f4f2b0360264c9f8ea13b257c7c39943c7cf6a2ec3a102ae'
+    ),
+    MODNetWeights.WEBCAM: ModelInfo(
+        url='https://github.com/yakhyo/modnet/releases/download/weights/modnet_webcam.onnx',
+        sha256='de03cc16f3c91f25b7c2f0b42ea1a8d34f40a752234f3887572655e744e55306'
+    ),
 }
 
 

uniface/detection/base.py

@@ -5,7 +5,7 @@
 from __future__ import annotations
 
 from abc import ABC, abstractmethod
-from typing import Any
+from typing import Any, Literal
 
 import numpy as np
 
@@ -119,3 +119,77 @@ class BaseDetector(ABC):
             List of detected Face objects.
         """
         return self.detect(image, **kwargs)
+
+    def _select_top_detections(
+        self,
+        detections: np.ndarray,
+        landmarks: np.ndarray,
+        max_num: int,
+        original_shape: tuple[int, int],
+        metric: Literal['default', 'max'] = 'max',
+        center_weight: float = 2.0,
+    ) -> tuple[np.ndarray, np.ndarray]:
+        """Filter detections to keep only top max_num faces.
+
+        Ranks faces by area and/or distance from image center, then selects
+        the top max_num detections.
+
+        Args:
+            detections: Array of shape (N, 5) as [x1, y1, x2, y2, confidence].
+            landmarks: Array of shape (N, 5, 2) for 5-point landmarks.
+            max_num: Maximum number of faces to keep. If 0 or >= N, returns all.
+            original_shape: Original image shape as (height, width).
+            metric: Ranking metric:
+                - 'max': Rank by bounding box area only.
+                - 'default': Rank by area minus center distance penalty.
+            center_weight: Weight for center distance penalty (only used with 'default' metric).
+
+        Returns:
+            Filtered (detections, landmarks) tuple with at most max_num faces.
+        """
+        if max_num <= 0 or detections.shape[0] <= max_num:
+            return detections, landmarks
+
+        # Calculate bounding box areas
+        area = (detections[:, 2] - detections[:, 0]) * (detections[:, 3] - detections[:, 1])
+
+        # Calculate offsets from image center
+        center_y, center_x = original_shape[0] // 2, original_shape[1] // 2
+        offsets = np.vstack(
+            [
+                (detections[:, 0] + detections[:, 2]) / 2 - center_x,
+                (detections[:, 1] + detections[:, 3]) / 2 - center_y,
+            ]
+        )
+        offset_dist_squared = np.sum(np.power(offsets, 2.0), axis=0)
+
+        # Calculate ranking scores based on metric
+        if metric == 'max':
+            scores = area
+        else:
+            scores = area - offset_dist_squared * center_weight
+
+        # Select top max_num by score
+        top_indices = np.argsort(scores)[::-1][:max_num]
+        return detections[top_indices], landmarks[top_indices]
+
+    @staticmethod
+    def _detections_to_faces(detections: np.ndarray, landmarks: np.ndarray) -> list[Face]:
+        """Convert detection arrays to Face objects.
+
+        Args:
+            detections: Array of shape (N, 5) as [x1, y1, x2, y2, confidence].
+            landmarks: Array of shape (N, 5, 2) for 5-point landmarks.
+
+        Returns:
+            List of Face objects.
+        """
+        faces = []
+        for i in range(detections.shape[0]):
+            face = Face(
+                bbox=detections[i, :4],
+                confidence=float(detections[i, 4]),
+                landmarks=landmarks[i],
+            )
+            faces.append(face)
+        return faces

uniface/detection/retinaface.py

@@ -208,42 +208,12 @@ class RetinaFace(BaseDetector):
         # Postprocessing
         detections, landmarks = self.postprocess(outputs, resize_factor, shape=(width, height))
 
-        if max_num > 0 and detections.shape[0] > max_num:
-            # Calculate area of detections
-            areas = (detections[:, 2] - detections[:, 0]) * (detections[:, 3] - detections[:, 1])
+        # Filter to top max_num faces if requested
+        detections, landmarks = self._select_top_detections(
+            detections, landmarks, max_num, (original_height, original_width), metric, center_weight
+        )
 
-            # Calculate offsets from image center
-            center = (original_height // 2, original_width // 2)
-            offsets = np.vstack(
-                [
-                    (detections[:, 0] + detections[:, 2]) / 2 - center[1],
-                    (detections[:, 1] + detections[:, 3]) / 2 - center[0],
-                ]
-            )
-            offset_dist_squared = np.sum(np.power(offsets, 2.0), axis=0)
-
-            # Calculate scores based on the chosen metric
-            if metric == 'max':
-                scores = areas
-            else:
-                scores = areas - offset_dist_squared * center_weight
-
-            # Sort by scores and select top `max_num`
-            sorted_indices = np.argsort(scores)[::-1][:max_num]
-
-            detections = detections[sorted_indices]
-            landmarks = landmarks[sorted_indices]
-
-        faces = []
-        for i in range(detections.shape[0]):
-            face = Face(
-                bbox=detections[i, :4],
-                confidence=float(detections[i, 4]),
-                landmarks=landmarks[i],
-            )
-            faces.append(face)
-
-        return faces
+        return self._detections_to_faces(detections, landmarks)
 
     def postprocess(
         self,

uniface/detection/scrfd.py

@@ -272,38 +272,9 @@ class SCRFD(BaseDetector):
         landmarks = landmarks[order, :, :]
         landmarks = landmarks[keep, :, :].astype(np.float32)
 
-        if 0 < max_num < detections.shape[0]:
-            # Calculate area of detections
-            area = (detections[:, 2] - detections[:, 0]) * (detections[:, 3] - detections[:, 1])
+        # Filter to top max_num faces if requested
+        detections, landmarks = self._select_top_detections(
+            detections, landmarks, max_num, (original_height, original_width), metric, center_weight
+        )
 
-            # Calculate offsets from image center
-            center = (original_height // 2, original_width // 2)
-            offsets = np.vstack(
-                [
-                    (detections[:, 0] + detections[:, 2]) / 2 - center[1],
-                    (detections[:, 1] + detections[:, 3]) / 2 - center[0],
-                ]
-            )
-
-            # Calculate scores based on the chosen metric
-            offset_dist_squared = np.sum(np.power(offsets, 2.0), axis=0)
-            if metric == 'max':
-                values = area
-            else:
-                values = area - offset_dist_squared * center_weight
-
-            # Sort by scores and select top `max_num`
-            sorted_indices = np.argsort(values)[::-1][:max_num]
-            detections = detections[sorted_indices]
-            landmarks = landmarks[sorted_indices]
-
-        faces = []
-        for i in range(detections.shape[0]):
-            face = Face(
-                bbox=detections[i, :4],
-                confidence=float(detections[i, 4]),
-                landmarks=landmarks[i],
-            )
-            faces.append(face)
-
-        return faces
+        return self._detections_to_faces(detections, landmarks)

uniface/detection/yolov5.py

@@ -4,10 +4,9 @@
 
 from typing import Any, Literal
 
-import cv2
 import numpy as np
 
-from uniface.common import non_max_suppression
+from uniface.common import letterbox_resize, non_max_suppression
 from uniface.constants import YOLOv5FaceWeights
 from uniface.log import Logger
 from uniface.model_store import verify_model_weights
@@ -140,45 +139,15 @@ class YOLOv5Face(BaseDetector):
             raise RuntimeError(f"Failed to initialize model session for '{model_path}'") from e
 
     def preprocess(self, image: np.ndarray) -> tuple[np.ndarray, float, tuple[int, int]]:
-        """
-        Preprocess image for inference.
+        """Preprocess image using letterbox resize.
 
         Args:
-            image (np.ndarray): Input image (BGR format)
+            image: Input image in BGR format.
 
         Returns:
-            Tuple[np.ndarray, float, Tuple[int, int]]: Preprocessed image, scale ratio, and padding
+            Tuple of (preprocessed_tensor, scale_ratio, padding).
         """
-        # Get original image shape
-        img_h, img_w = image.shape[:2]
-
-        # Calculate scale ratio
-        scale = min(self.input_size / img_h, self.input_size / img_w)
-        new_h, new_w = int(img_h * scale), int(img_w * scale)
-
-        # Resize image
-        img_resized = cv2.resize(image, (new_w, new_h), interpolation=cv2.INTER_LINEAR)
-
-        # Create padded image
-        img_padded = np.full((self.input_size, self.input_size, 3), 114, dtype=np.uint8)
-
-        # Calculate padding
-        pad_h = (self.input_size - new_h) // 2
-        pad_w = (self.input_size - new_w) // 2
-
-        # Place resized image in center
-        img_padded[pad_h : pad_h + new_h, pad_w : pad_w + new_w] = img_resized
-
-        # Convert to RGB and normalize
-        img_rgb = cv2.cvtColor(img_padded, cv2.COLOR_BGR2RGB)
-        img_normalized = img_rgb.astype(np.float32) / 255.0
-
-        # Transpose to CHW format (HWC -> CHW) and add batch dimension
-        img_transposed = np.transpose(img_normalized, (2, 0, 1))
-        img_batch = np.expand_dims(img_transposed, axis=0)
-        img_batch = np.ascontiguousarray(img_batch)
-
-        return img_batch, scale, (pad_w, pad_h)
+        return letterbox_resize(image, self.input_size)
 
     def inference(self, input_tensor: np.ndarray) -> list[np.ndarray]:
         """Perform model inference on the preprocessed image tensor.
@@ -337,38 +306,9 @@ class YOLOv5Face(BaseDetector):
         if len(detections) == 0:
             return []
 
-        if 0 < max_num < detections.shape[0]:
-            # Calculate area of detections
-            area = (detections[:, 2] - detections[:, 0]) * (detections[:, 3] - detections[:, 1])
+        # Filter to top max_num faces if requested
+        detections, landmarks = self._select_top_detections(
+            detections, landmarks, max_num, (original_height, original_width), metric, center_weight
+        )
 
-            # Calculate offsets from image center
-            center = (original_height // 2, original_width // 2)
-            offsets = np.vstack(
-                [
-                    (detections[:, 0] + detections[:, 2]) / 2 - center[1],
-                    (detections[:, 1] + detections[:, 3]) / 2 - center[0],
-                ]
-            )
-
-            # Calculate scores based on the chosen metric
-            offset_dist_squared = np.sum(np.power(offsets, 2.0), axis=0)
-            if metric == 'max':
-                values = area
-            else:
-                values = area - offset_dist_squared * center_weight
-
-            # Sort by scores and select top `max_num`
-            sorted_indices = np.argsort(values)[::-1][:max_num]
-            detections = detections[sorted_indices]
-            landmarks = landmarks[sorted_indices]
-
-        faces = []
-        for i in range(detections.shape[0]):
-            face = Face(
-                bbox=detections[i, :4],
-                confidence=float(detections[i, 4]),
-                landmarks=landmarks[i],
-            )
-            faces.append(face)
-
-        return faces
+        return self._detections_to_faces(detections, landmarks)