chore: Update docstrings and comments (#119)

Bumps [gitpython](https://github.com/gitpython-developers/GitPython) from 3.1.49 to 3.1.50.
- [Release notes](https://github.com/gitpython-developers/GitPython/releases)
- [Changelog](https://github.com/gitpython-developers/GitPython/blob/main/CHANGES)
- [Commits](https://github.com/gitpython-developers/GitPython/compare/3.1.49...3.1.50)

---
updated-dependencies:
- dependency-name: gitpython
  dependency-version: 3.1.50
  dependency-type: indirect
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>

.github/workflows/ci.yml

@@ -1,4 +1,4 @@
-name: Build
+name: CI
 
 on:
   push:
@@ -17,8 +17,8 @@ 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: "3.11"
       - uses: pre-commit/action@v3.0.1
@@ -37,6 +37,8 @@ jobs:
             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
@@ -48,28 +50,25 @@ 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
+      - name: Install uv
+        uses: astral-sh/setup-uv@v6
         with:
+          enable-cache: true
           python-version: ${{ matrix.python-version }}
-          cache: "pip"
 
       - name: Install dependencies
-        run: |
-          python -m pip install --upgrade pip
-          python -m pip install .[dev]
+        run: uv sync --locked --extra cpu --extra dev
 
       - name: Check ONNX Runtime providers
-        run: |
-          python -c "import onnxruntime as ort; print('Available providers:', ort.get_available_providers())"
+        run: uv run python -c "import onnxruntime as ort; print('Available providers:', ort.get_available_providers())"
 
       - name: Run tests
-        run: pytest -v --tb=short
+        run: uv run pytest -v --tb=short
 
       - name: Test package imports
-        run: python -c "import uniface; print(f'uniface {uniface.__version__} loaded with {len(uniface.__all__)} exports')"
+        run: uv run python -c "import uniface; print(f'uniface {uniface.__version__} loaded with {len(uniface.__all__)} exports')"
 
   build:
     runs-on: ubuntu-latest
@@ -78,10 +77,10 @@ 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

@@ -1,9 +1,6 @@
-name: Deploy docs
+name: Deploy Documentation
 
 on:
-  push:
-    tags:
-      - "v*.*.*"
   workflow_dispatch:
 
 permissions:
@@ -13,26 +10,28 @@ 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
+      - name: Install uv
+        uses: astral-sh/setup-uv@v6
         with:
+          enable-cache: true
           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
+        run: uv sync --locked --extra docs
 
       - name: Build docs
         env:
           MKDOCS_GIT_COMMITTERS_APIKEY: ${{ secrets.MKDOCS_GIT_COMMITTERS_APIKEY }}
-        run: mkdocs build --strict
+        run: uv run mkdocs build --strict
 
       - name: Deploy to GitHub Pages
         uses: peaceiris/actions-gh-pages@v4
+        env:
+          FORCE_JAVASCRIPT_ACTIONS_TO_NODE24: true
         with:
           github_token: ${{ secrets.GITHUB_TOKEN }}
           publish_dir: ./site

.github/workflows/pipeline.yml

@@ -0,0 +1,229 @@
+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: Install uv
+        uses: astral-sh/setup-uv@v6
+        with:
+          enable-cache: true
+          python-version: ${{ matrix.python-version }}
+
+      - name: Install dependencies
+        run: uv sync --locked --extra cpu --extra dev
+
+      - name: Run tests
+        run: uv 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__ = '${{ inputs.version }}'", text, count=1, flags=re.M)
+          if new == text:
+              raise SystemExit("Failed to update __version__ in uniface/__init__.py")
+          p.write_text(new)
+          EOF
+
+      - name: Install uv
+        uses: astral-sh/setup-uv@v6
+        with:
+          enable-cache: true
+          python-version: "3.11"
+
+      - name: Refresh uv.lock with new project version
+        run: uv lock --upgrade-package uniface
+
+      - name: Commit, tag, push
+        run: |
+          git config user.name "github-actions[bot]"
+          git config user.email "41898282+github-actions[bot]@users.noreply.github.com"
+          git add pyproject.toml uniface/__init__.py uv.lock
+          git commit -m "chore: Release v${{ 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: ${{ secrets.PYPI_API_TOKEN }}
+        run: twine upload dist/*
+
+      - name: Create GitHub Release
+        uses: softprops/action-gh-release@v2
+        with:
+          tag_name: v${{ 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: Install uv
+        uses: astral-sh/setup-uv@v6
+        with:
+          enable-cache: true
+          python-version: "3.11"
+
+      - name: Install dependencies
+        run: uv sync --locked --extra docs
+
+      - name: Build docs
+        env:
+          MKDOCS_GIT_COMMITTERS_APIKEY: ${{ secrets.MKDOCS_GIT_COMMITTERS_APIKEY }}
+        run: uv run mkdocs build --strict
+
+      - name: Deploy to GitHub Pages
+        uses: peaceiris/actions-gh-pages@v4
+        env:
+          FORCE_JAVASCRIPT_ACTIONS_TO_NODE24: true
+        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", "3.11", "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: "3.11"
-          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:
-          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

@@ -21,25 +21,31 @@ Thank you for considering contributing to UniFace! We welcome contributions of a
 
 ## Development Setup
 
+We use [uv](https://docs.astral.sh/uv/) for reproducible dev installs. The committed `uv.lock` pins every transitive dependency so contributors and CI resolve to identical versions.
+
 ```bash
+# Install uv (https://docs.astral.sh/uv/getting-started/installation/)
+curl -LsSf https://astral.sh/uv/install.sh | sh
+
 git clone https://github.com/yakhyo/uniface.git
 cd uniface
-pip install -e ".[dev]"
+
+# Sync runtime + cpu + dev extras from uv.lock (use --extra gpu instead of cpu for CUDA)
+uv sync --extra cpu --extra dev
 ```
 
+`uv sync` creates a project-local `.venv/` and installs everything pinned in `uv.lock`. Run commands with `uv run <cmd>` (e.g. `uv run pytest`), or activate the venv with `source .venv/bin/activate`.
+
 ### Setting Up Pre-commit Hooks
 
-We use [pre-commit](https://pre-commit.com/) to ensure code quality and consistency. Install and configure it:
+We use [pre-commit](https://pre-commit.com/) to ensure code quality and consistency. `pre-commit` is included in the `[dev]` extra, so it's already installed after `uv sync`.
 
 ```bash
-# Install pre-commit
-pip install pre-commit
-
 # Install the git hooks
-pre-commit install
+uv run pre-commit install
 
 # (Optional) Run against all files
-pre-commit run --all-files
+uv run pre-commit run --all-files
 ```
 
 Once installed, pre-commit will automatically run on every commit to check:
@@ -59,12 +65,12 @@ This project uses [Ruff](https://docs.astral.sh/ruff/) for linting and formattin
 #### General Rules
 
 - **Line length:** 120 characters maximum
-- **Python version:** 3.11+ (use modern syntax)
+- **Python version:** 3.10+ (use modern syntax)
 - **Quote style:** Single quotes for strings, double quotes for docstrings
 
 #### Type Hints
 
-Use modern Python 3.11+ type hints (PEP 585 and PEP 604):
+Use modern Python 3.10+ type hints (PEP 585 and PEP 604):
 
 ```python
 # Preferred (modern)
@@ -188,6 +194,45 @@ Example notebooks demonstrating library usage:
 | 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?
 
 Open an issue or start a discussion on GitHub.

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">
 
@@ -14,22 +14,23 @@
 </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)
+- **Facial Landmarks** — 106-point (2d106det) and 98 / 68-point (PIPNet) landmark localization (separate from the 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
@@ -40,25 +41,60 @@
 
 ---
 
+## 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 store**
@@ -156,6 +192,32 @@ for face in faces:
 
 ---
 
+## 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 |
@@ -164,7 +226,7 @@ for face in faces:
 | [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 |
+| [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 |
@@ -172,6 +234,7 @@ for face in faces:
 | [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 |
 
 ---
 
@@ -212,6 +275,8 @@ https://yakhyo.github.io/uniface/concepts/execution-providers/
 | Recognition | MS1MV2 | MobileFace, SphereFace |
 | Recognition | WebFace600K | ArcFace |
 | Recognition | WebFace4M / 12M | AdaFace |
+| Recognition | MS1MV2 | EdgeFace |
+| Landmarks | WFLW, 300W+CelebA | PIPNet (98 / 68 pts) |
 | Gaze | Gaze360 | MobileGaze |
 | Head Pose | 300W-LP | HeadPose (ResNet, MobileNet) |
 | Parsing | CelebAMask-HQ | BiSeNet |
@@ -243,10 +308,13 @@ 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 |
+| Landmarks | [pipnet-onnx](https://github.com/yakhyo/pipnet-onnx) | - | PIPNet 98 / 68-point 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 |
 
@@ -270,3 +338,6 @@ 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/coordinate-systems.md

@@ -110,6 +110,28 @@ landmarks = landmarker.get_landmarks(image, face.bbox)
 | 63-86 | Eyes | 24 |
 | 87-105 | Mouth | 19 |
 
+### 98 / 68-Point Landmarks (PIPNet)
+
+Returned by `PIPNet`. The variant determines the layout:
+
+```python
+from uniface.constants import PIPNetWeights
+from uniface.landmark import PIPNet
+
+# 98-point WFLW layout (default)
+landmarks = PIPNet().get_landmarks(image, face.bbox)
+# Shape: (98, 2)
+
+# 68-point 300W layout
+landmarks = PIPNet(model_name=PIPNetWeights.DW300_CELEBA_68).get_landmarks(image, face.bbox)
+# Shape: (68, 2)
+```
+
+The 98-point output follows the standard [WFLW](https://wywu.github.io/projects/LAB/WFLW.html) layout
+(33 face-contour points, eyebrow/eye/nose/mouth groups). The 68-point output follows the standard
+[300W / iBUG](https://ibug.doc.ic.ac.uk/resources/300-W/) layout. Coordinates are in original-image
+pixel space, identical in convention to `Landmark106`.
+
 ---
 
 ## Face Crop

docs/concepts/execution-providers.md

@@ -43,7 +43,7 @@ All **ONNX-based** model classes accept the `providers` parameter:
 
 - Detection: `RetinaFace`, `SCRFD`, `YOLOv5Face`, `YOLOv8Face`
 - Recognition: `ArcFace`, `AdaFace`, `MobileFace`, `SphereFace`
-- Landmarks: `Landmark106`
+- Landmarks: `Landmark106`, `PIPNet`
 - Gaze: `MobileGaze`
 - Parsing: `BiSeNet`, `XSeg`
 - Attributes: `AgeGender`, `FairFace`
@@ -93,7 +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:
@@ -110,7 +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]
@@ -140,7 +140,7 @@ else:
 CPU execution is always available:
 
 ```bash
-pip install uniface
+pip install uniface[cpu]
 ```
 
 Works on all platforms without additional configuration.
@@ -215,7 +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

@@ -43,7 +43,7 @@ class Face:
     # Required (from detection)
     bbox: np.ndarray        # [x1, y1, x2, y2]
     confidence: float       # 0.0 to 1.0
-    landmarks: np.ndarray   # (5, 2) or (106, 2)
+    landmarks: np.ndarray   # (5, 2) from detectors. Dense landmarkers return (106, 2), (98, 2), or (68, 2).
 
     # Optional (enriched by analyzers)
     embedding: np.ndarray | None = None

docs/concepts/model-cache-offline.md

@@ -194,6 +194,8 @@ If a model fails verification, it's re-downloaded automatically.
 | Model | Size | Download |
 |-------|------|----------|
 | Landmark106 | 14 MB | ✅ |
+| PIPNet WFLW-98 | 47 MB | ✅ |
+| PIPNet 300W+CelebA-68 | 46 MB | ✅ |
 | AgeGender | 8 MB | ✅ |
 | FairFace | 44 MB | ✅ |
 | Gaze ResNet34 | 82 MB | ✅ |

docs/concepts/overview.md

@@ -26,6 +26,7 @@ graph TB
         HPOSE[Head Pose]
         PARSE[Parsing]
         SPOOF[Anti-Spoofing]
+        MATT[Matting]
         PRIV[Privacy]
     end
 
@@ -42,6 +43,7 @@ graph TB
     end
 
     IMG --> DET
+    IMG --> MATT
     DET --> REC
     DET --> LMK
     DET --> ATTR
@@ -62,16 +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:
@@ -115,11 +115,12 @@ 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
+├── landmark/       # Dense landmarks (Landmark106 = 106 pts, PIPNet = 98 / 68 pts)
 ├── attribute/      # Age, gender, emotion, race
 ├── parsing/        # Face semantic segmentation
+├── matting/        # Portrait matting (MODNet)
 ├── gaze/           # Gaze estimation
 ├── headpose/       # Head pose estimation
 ├── spoofing/       # Anti-spoofing

docs/contributing.md

@@ -6,16 +6,20 @@ Thank you for contributing to UniFace!
 
 ## Quick Start
 
+We use [uv](https://docs.astral.sh/uv/) for reproducible dev installs (lockfile-pinned).
+
 ```bash
+# Install uv first: https://docs.astral.sh/uv/getting-started/installation/
+
 # Clone
 git clone https://github.com/yakhyo/uniface.git
 cd uniface
 
-# Install dev dependencies
-pip install -e ".[dev]"
+# Install runtime + cpu + dev extras from uv.lock (--extra gpu for CUDA)
+uv sync --extra cpu --extra dev
 
 # Run tests
-pytest
+uv run pytest
 ```
 
 ---
@@ -39,10 +43,43 @@ ruff check . --fix
 
 ## Pre-commit Hooks
 
+`pre-commit` is included in the `[dev]` extra, so `uv sync` already installs it.
+
 ```bash
-pip install pre-commit
-pre-commit install
-pre-commit run --all-files
+uv run pre-commit install
+uv run 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
 ```
 
 ---
@@ -67,6 +104,14 @@ 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

@@ -12,6 +12,7 @@ Overview of all training datasets and evaluation benchmarks used by UniFace mode
 | Recognition | [MS1MV2](#ms1mv2)                                | 5.8M images, 85.7K IDs | MobileFace, SphereFace                      |
 | Recognition | [WebFace600K](#webface600k)                      | 600K images            | ArcFace                                     |
 | Recognition | [WebFace4M / WebFace12M](#webface4m--webface12m) | 4M / 12M images        | AdaFace                                     |
+| Landmarks   | [WFLW](#wflw) / [300W+CelebA](#300w--celeba)     | 10K / 3.8K labeled + 202.6K unlabeled | PIPNet (98 / 68 pts)         |
 | Gaze        | [Gaze360](#gaze360)                              | 238 subjects           | MobileGaze                                  |
 | Parsing     | [CelebAMask-HQ](#celebamask-hq)                  | 30K images             | BiSeNet                                     |
 | Attributes  | [CelebA](#celeba)                                | 200K images            | AgeGender                                   |
@@ -126,6 +127,41 @@ Large-scale dataset with wide variations in pose, age, illumination, ethnicity,
 
 ---
 
+### Facial Landmarks
+
+#### WFLW
+
+Wider Facial Landmarks in-the-Wild — a 98-point landmark dataset whose images come from
+WIDER FACE. Used to train the supervised PIPNet 98-point variant shipped with UniFace.
+
+| Property   | Value                                  |
+| ---------- | -------------------------------------- |
+| Images     | 10,000 (7,500 train / 2,500 test)      |
+| Annotation | 98 manually labeled landmarks per face |
+| Used by    | PIPNet WFLW-98                         |
+
+!!! info "Reference"
+    **Project page**: [WFLW dataset](https://wywu.github.io/projects/LAB/WFLW.html)
+
+---
+
+#### 300W + CelebA
+
+The 68-point PIPNet variant is trained in a generalizable semi-supervised setting (GSSL):
+labeled images come from 300W and unlabeled images come from CelebA.
+
+| Property        | Value                                                                            |
+| --------------- | -------------------------------------------------------------------------------- |
+| Labeled images  | 3,837 (3,148 train: LFPW train + HELEN train + AFW; 689 test: LFPW test + HELEN test + iBUG) |
+| Unlabeled       | 202,599 (full CelebA; bounding boxes from RetinaFace per the PIPNet paper)        |
+| Annotation      | 68-point iBUG layout                                                             |
+| Used by         | PIPNet 300W+CelebA-68                                                            |
+
+!!! info "Reference"
+    **Paper**: [PIPNet (Pixel-in-Pixel Net)](https://arxiv.org/abs/2003.03771) (IJCV 2021)
+
+---
+
 ### Gaze Estimation
 
 #### Gaze360

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 }
@@ -31,17 +31,17 @@ 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>
 ### :material-map-marker: Landmarks
-Accurate 106-point facial landmark localization for detailed face analysis.
+Dense facial landmark localization — 106-point (2d106det) and 98 / 68-point (PIPNet) variants.
 </div>
 
 <div class="feature-card" markdown>
@@ -90,14 +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]
 ```
@@ -106,7 +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 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.
-
----
-
 ### 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 |
-|---------|---------------|---------|
+| 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 |
-| `onnxruntime-gpu` | `uniface[gpu]` | CUDA acceleration |
-| `torch` | `pip install torch` | Emotion model uses TorchScript |
+| `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 +20,7 @@ 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 |
+| PIPNet | [yakhyo/pipnet-onnx](https://github.com/yakhyo/pipnet-onnx) — meanface tables vendored from [jhb86253817/PIPNet](https://github.com/jhb86253817/PIPNet) | MIT |

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
@@ -178,6 +196,26 @@ Facial landmark localization model.
 
 ---
 
+### PIPNet (98 / 68 points)
+
+PIPNet (Pixel-in-Pixel Net) facial landmark detector. ResNet-18 backbone, 256×256 input.
+
+| Model Name | Points | Backbone | Dataset | Size |
+| ---------- | ------ | -------- | ------- | ---- |
+| `WFLW_98` :material-check-circle: | 98 | ResNet-18 | WFLW (supervised) | 47 MB |
+| `DW300_CELEBA_68` | 68 | ResNet-18 | 300W+CelebA (GSSL) | 46 MB |
+
+!!! info "Reference"
+    **Paper**: [PIPNet: Towards Efficient Facial Landmark Detection in the Wild](https://arxiv.org/abs/2003.03771) (IJCV 2021)
+
+    **Source**: [yakhyo/pipnet-onnx](https://github.com/yakhyo/pipnet-onnx) — ONNX export from [jhb86253817/PIPNet](https://github.com/jhb86253817/PIPNet)
+
+!!! note "Auto-selected meanface"
+    Both variants share the same architecture; the number of landmarks (and the matching
+    meanface table) is inferred from the ONNX output channel count.
+
+---
+
 ## Attribute Analysis Models
 
 ### Age & Gender Detection
@@ -353,6 +391,36 @@ 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
@@ -402,8 +470,10 @@ See [Model Cache & Offline Use](concepts/model-cache-offline.md) for full detail
 - **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
+- **PIPNet**: [yakhyo/pipnet-onnx](https://github.com/yakhyo/pipnet-onnx) - PIPNet ONNX export and inference (from [jhb86253817/PIPNet](https://github.com/jhb86253817/PIPNet))
 - **InsightFace**: [deepinsight/insightface](https://github.com/deepinsight/insightface) - Model architectures and pretrained weights
 
 ### Papers
@@ -414,4 +484,6 @@ 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)
+- **PIPNet**: [Towards Efficient Facial Landmark Detection in the Wild](https://arxiv.org/abs/2003.03771)

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

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
@@ -286,6 +291,6 @@ python tools/detect.py --source image.jpg
 ## See Also
 
 - [Recognition Module](recognition.md) - Extract embeddings from detected faces
-- [Landmarks Module](landmarks.md) - Get 106-point landmarks
+- [Landmarks Module](landmarks.md) - Get 106 / 98 / 68-point dense landmarks
 - [Image Pipeline Recipe](../recipes/image-pipeline.md) - Complete detection workflow
 - [Concepts: Thresholds](../concepts/thresholds-calibration.md) - Tuning detection parameters

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

docs/modules/headpose.md

@@ -2,6 +2,11 @@
 
 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

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
@@ -9,6 +14,8 @@ Facial landmark detection provides precise localization of facial features.
 | Model | Points | Size |
 |-------|--------|------|
 | **Landmark106** | 106 | 14 MB |
+| **PIPNet (WFLW-98)** | 98 | 47 MB |
+| **PIPNet (300W+CelebA-68)** | 68 | 46 MB |
 
 !!! info "5-Point Landmarks"
     Basic 5-point landmarks are included with all detection models (RetinaFace, SCRFD, YOLOv5-Face, YOLOv8-Face).
@@ -74,6 +81,48 @@ mouth = landmarks[87:106]
 
 ---
 
+## PIPNet (98 / 68 points)
+
+PIPNet (Pixel-in-Pixel Net) is a high-accuracy facial landmark detector. UniFace ships
+two ONNX variants that share a ResNet-18 backbone and 256×256 input — the only difference
+is the number of points and the dataset they were trained on.
+
+### Basic Usage
+
+```python
+from uniface.detection import RetinaFace
+from uniface.landmark import PIPNet
+
+detector = RetinaFace()
+landmarker = PIPNet()  # Default: 98 points (WFLW)
+
+faces = detector.detect(image)
+if faces:
+    landmarks = landmarker.get_landmarks(image, faces[0].bbox)
+    print(f"Landmarks shape: {landmarks.shape}")  # (98, 2)
+```
+
+### 68-Point Variant (300W+CelebA, GSSL)
+
+```python
+from uniface.constants import PIPNetWeights
+from uniface.landmark import PIPNet
+
+landmarker = PIPNet(model_name=PIPNetWeights.DW300_CELEBA_68)
+landmarks = landmarker.get_landmarks(image, face.bbox)
+print(landmarks.shape)  # (68, 2)
+```
+
+### Notes
+
+- The number of landmarks is read from the ONNX output and the matching meanface
+  table is selected automatically — there is no `num_lms=` argument.
+- PIPNet uses an asymmetric crop around the bbox (+10% left / right / bottom,
+  −10% top) and ImageNet normalization. This is handled internally.
+- Output landmarks are in original-image pixel coordinates as `float32`.
+
+---
+
 ## 5-Point Landmarks (Detection)
 
 All detection models provide 5-point landmarks:
@@ -237,9 +286,17 @@ def estimate_head_pose(landmarks, image_shape):
 ## Factory Function
 
 ```python
+from uniface.constants import PIPNetWeights
 from uniface.landmark import create_landmarker
 
-landmarker = create_landmarker()  # Returns Landmark106
+# Default: 106-point InsightFace model
+landmarker = create_landmarker()
+
+# 98-point PIPNet (WFLW)
+landmarker = create_landmarker('pipnet')
+
+# 68-point PIPNet (300W+CelebA)
+landmarker = create_landmarker('pipnet', model_name=PIPNetWeights.DW300_CELEBA_68)
 ```
 
 ---

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/notebooks.md

@@ -12,7 +12,7 @@ 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 |
@@ -20,6 +20,7 @@ Run UniFace examples directly in your browser with Google Colab, or download and
 | [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 |
 
 ---
 
@@ -33,7 +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

@@ -166,7 +166,9 @@ Face 2: Female, 20-29, White
 
 ---
 
-## Facial Landmarks (106 Points)
+## Facial Landmarks (106 / 98 / 68 Points)
+
+UniFace ships two dense-landmark families. Pick whichever fits your downstream task:
 
 ```python
 import cv2
@@ -174,14 +176,14 @@ from uniface.detection import RetinaFace
 from uniface.landmark import Landmark106
 
 detector = RetinaFace()
-landmarker = Landmark106()
+landmarker = Landmark106()  # 106-point InsightFace 2d106det model
 
 image = cv2.imread("photo.jpg")
 faces = detector.detect(image)
 
 if faces:
     landmarks = landmarker.get_landmarks(image, faces[0].bbox)
-    print(f"Detected {len(landmarks)} landmarks")
+    print(f"Detected {len(landmarks)} landmarks")  # 106
 
     # Draw landmarks
     for x, y in landmarks.astype(int):
@@ -190,6 +192,21 @@ if faces:
     cv2.imwrite("landmarks.jpg", image)
 ```
 
+**PIPNet (98 / 68 points)** — ResNet-18 backbone trained on WFLW (98 pts) or 300W+CelebA (68 pts):
+
+```python
+from uniface.constants import PIPNetWeights
+from uniface.landmark import PIPNet
+
+# 98-point WFLW model (default)
+landmarker_98 = PIPNet()
+
+# 68-point 300W+CelebA model
+landmarker_68 = PIPNet(model_name=PIPNetWeights.DW300_CELEBA_68)
+
+landmarks = landmarker_98.get_landmarks(image, faces[0].bbox)  # (98, 2)
+```
+
 ---
 
 ## Gaze Estimation
@@ -280,6 +297,34 @@ 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:
@@ -437,7 +482,8 @@ For detailed model comparisons and benchmarks, see the [Model Zoo](models.md).
 | Task | Available Models |
 |------|------------------|
 | Detection | `RetinaFace`, `SCRFD`, `YOLOv5Face`, `YOLOv8Face` |
-| Recognition | `ArcFace`, `AdaFace`, `MobileFace`, `SphereFace` |
+| Recognition | `ArcFace`, `AdaFace`, `EdgeFace`, `MobileFace`, `SphereFace` |
+| Landmarks | `Landmark106` (106 pts), `PIPNet` (98 / 68 pts) |
 | Tracking | `BYTETracker` |
 | Gaze | `MobileGaze` (ResNet18/34/50, MobileNetV2, MobileOneS0) |
 | Head Pose | `HeadPose` (ResNet18/34/50, MobileNetV2/V3) |
@@ -485,7 +531,7 @@ python -c "import platform; print(platform.machine())"
 from uniface.detection import RetinaFace, SCRFD
 from uniface.recognition import ArcFace, AdaFace
 from uniface.attribute import AgeGender, FairFace
-from uniface.landmark import Landmark106
+from uniface.landmark import Landmark106, PIPNet
 from uniface.gaze import MobileGaze
 from uniface.headpose import HeadPose
 from uniface.parsing import BiSeNet, XSeg

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
 
@@ -150,6 +150,7 @@ 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

pyproject.toml

@@ -1,7 +1,7 @@
 [project]
 name = "uniface"
-version = "3.3.0"
-description = "UniFace: A Comprehensive Library for Face Detection, Recognition, Tracking, Landmark Analysis, Face Parsing, Gaze Estimation, Age, and Gender Detection"
+version = "3.6.0"
+description = "UniFace: A Unified Face Analysis Library for Python"
 readme = "README.md"
 license = "MIT"
 authors = [{ name = "Yakhyokhuja Valikhujaev", email = "yakhyo9696@gmail.com" }]
@@ -44,7 +44,6 @@ classifiers = [
 dependencies = [
     "numpy>=1.21.0",
     "opencv-python>=4.5.0",
-    "onnxruntime>=1.16.0",
     "scikit-image>=0.22.0",
     "scipy>=1.7.0",
     "requests>=2.28.0",
@@ -52,8 +51,21 @@ dependencies = [
 ]
 
 [project.optional-dependencies]
-dev = ["pytest>=7.0.0", "ruff>=0.4.0"]
-gpu = ["onnxruntime-gpu>=1.16.0"]
+cpu = [
+    "onnxruntime>=1.16.0; python_version >= '3.11'",
+    "onnxruntime>=1.16.0,<1.24; python_version < '3.11'",
+]
+gpu = [
+    "onnxruntime-gpu>=1.16.0; python_version >= '3.11'",
+    "onnxruntime-gpu>=1.16.0,<1.24; python_version < '3.11'",
+]
+dev = ["pytest>=7.0.0", "ruff>=0.4.0", "pre-commit>=3.0.0"]
+docs = [
+    "mkdocs-material",
+    "pymdown-extensions",
+    "mkdocs-git-committers-plugin-2",
+    "mkdocs-git-revision-date-localized-plugin",
+]
 
 [project.urls]
 Homepage = "https://github.com/yakhyo/uniface"

requirements.txt

@@ -1,7 +0,0 @@
-numpy>=1.21.0
-opencv-python>=4.5.0
-onnxruntime>=1.16.0
-scikit-image>=0.22.0
-scipy>=1.7.0
-requests>=2.28.0
-tqdm>=4.64.0

tests/test_draw.py

@@ -1,61 +0,0 @@
-# Copyright 2025-2026 Yakhyokhuja Valikhujaev
-# Author: Yakhyokhuja Valikhujaev
-# GitHub: https://github.com/yakhyo
-
-from __future__ import annotations
-
-import numpy as np
-
-from uniface.draw import draw_gaze
-
-
-def _compute_gaze_delta(bbox: np.ndarray, pitch: float, yaw: float) -> tuple[int, int]:
-    """Replicate draw_gaze dx/dy math for verification."""
-    x_min, _, x_max, _ = map(int, bbox[:4])
-    length = x_max - x_min
-    dx = int(-length * np.sin(yaw) * np.cos(pitch))
-    dy = int(-length * np.sin(pitch))
-    return dx, dy
-
-
-def test_draw_gaze_yaw_only_moves_horizontally():
-    """Yaw-only input (pitch=0) should produce horizontal displacement only."""
-    image = np.zeros((200, 200, 3), dtype=np.uint8)
-    bbox = np.array([50, 50, 150, 150], dtype=np.float32)
-
-    yaw = 0.5
-    pitch = 0.0
-    dx, dy = _compute_gaze_delta(bbox, pitch, yaw)
-
-    assert dx != 0, 'Yaw-only should produce horizontal displacement'
-    assert dy == 0, 'Yaw-only should produce zero vertical displacement'
-
-    # Should not raise
-    draw_gaze(image, bbox, pitch, yaw, draw_bbox=False, draw_angles=False)
-
-
-def test_draw_gaze_pitch_only_moves_vertically():
-    """Pitch-only input (yaw=0) should produce vertical displacement only."""
-    image = np.zeros((200, 200, 3), dtype=np.uint8)
-    bbox = np.array([50, 50, 150, 150], dtype=np.float32)
-
-    yaw = 0.0
-    pitch = 0.5
-    dx, dy = _compute_gaze_delta(bbox, pitch, yaw)
-
-    assert dx == 0, 'Pitch-only should produce zero horizontal displacement'
-    assert dy != 0, 'Pitch-only should produce vertical displacement'
-
-    # Should not raise
-    draw_gaze(image, bbox, pitch, yaw, draw_bbox=False, draw_angles=False)
-
-
-def test_draw_gaze_modifies_image():
-    """draw_gaze should modify the image in place."""
-    image = np.zeros((200, 200, 3), dtype=np.uint8)
-    bbox = np.array([50, 50, 150, 150], dtype=np.float32)
-
-    original = image.copy()
-    draw_gaze(image, bbox, 0.3, 0.3)
-
-    assert not np.array_equal(image, original), 'draw_gaze should modify the image'

tests/test_factory.py

@@ -91,6 +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.
@@ -124,6 +130,25 @@ def test_create_landmarker_invalid_method():
         create_landmarker('invalid_method')
 
 
+def test_create_landmarker_pipnet_default():
+    """create_landmarker('pipnet') returns a PIPNet (98 points by default)."""
+    from uniface.landmark import PIPNet
+
+    landmarker = create_landmarker('pipnet')
+    assert isinstance(landmarker, PIPNet), 'Should return PIPNet instance'
+    assert landmarker.num_lms == 98
+
+
+def test_create_landmarker_pipnet_68():
+    """create_landmarker('pipnet', model_name=...) selects the 68-point variant."""
+    from uniface.constants import PIPNetWeights
+    from uniface.landmark import PIPNet
+
+    landmarker = create_landmarker('pipnet', model_name=PIPNetWeights.DW300_CELEBA_68)
+    assert isinstance(landmarker, PIPNet), 'Should return PIPNet instance'
+    assert landmarker.num_lms == 68
+
+
 # list_available_detectors tests
 def test_list_available_detectors():
     """
@@ -183,6 +208,17 @@ def test_landmarker_inference_from_factory():
     assert landmarks.shape == (106, 2), 'Should return 106 landmarks'
 
 
+def test_pipnet_landmarker_inference_from_factory():
+    """PIPNet landmarker created from factory can perform inference."""
+    landmarker = create_landmarker('pipnet')
+    mock_image = np.random.randint(0, 255, (640, 640, 3), dtype=np.uint8)
+    mock_bbox = [100, 100, 300, 300]
+
+    landmarks = landmarker.get_landmarks(mock_image, mock_bbox)
+    assert landmarks is not None, 'Landmarker should return landmarks'
+    assert landmarks.shape == (98, 2), 'Should return 98 landmarks'
+
+
 def test_multiple_detector_creation():
     """
     Test that multiple detectors can be created independently.

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_pipnet_landmark.py

@@ -0,0 +1,132 @@
+# 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 PIPNetWeights
+from uniface.landmark import PIPNet
+
+
+@pytest.fixture(scope='module', params=[PIPNetWeights.WFLW_98, PIPNetWeights.DW300_CELEBA_68])
+def pipnet_model(request):
+    return PIPNet(model_name=request.param)
+
+
+@pytest.fixture
+def mock_image():
+    return np.random.randint(0, 255, (640, 640, 3), dtype=np.uint8)
+
+
+@pytest.fixture
+def mock_bbox():
+    return [100, 100, 300, 300]
+
+
+def _expected_n_lms(model: PIPNet) -> int:
+    return 98 if model.num_lms == 98 else 68
+
+
+def test_model_initialization(pipnet_model):
+    assert pipnet_model is not None, 'PIPNet model initialization failed.'
+    assert pipnet_model.num_lms in (68, 98), f'Unexpected num_lms: {pipnet_model.num_lms}'
+    assert pipnet_model.input_h == pipnet_model.input_w == 256
+
+
+def test_landmark_detection(pipnet_model, mock_image, mock_bbox):
+    landmarks = pipnet_model.get_landmarks(mock_image, mock_bbox)
+    n = _expected_n_lms(pipnet_model)
+    assert landmarks.shape == (n, 2), f'Expected shape ({n}, 2), got {landmarks.shape}'
+
+
+def test_landmark_dtype(pipnet_model, mock_image, mock_bbox):
+    landmarks = pipnet_model.get_landmarks(mock_image, mock_bbox)
+    assert landmarks.dtype == np.float32, f'Expected float32, got {landmarks.dtype}'
+
+
+def test_landmark_coordinates_within_image(pipnet_model, mock_image, mock_bbox):
+    landmarks = pipnet_model.get_landmarks(mock_image, mock_bbox)
+    n = _expected_n_lms(pipnet_model)
+
+    x_coords = landmarks[:, 0]
+    y_coords = landmarks[:, 1]
+
+    x1, y1, x2, y2 = mock_bbox
+    margin = 50
+
+    x_in_bounds = int(np.sum((x_coords >= x1 - margin) & (x_coords <= x2 + margin)))
+    y_in_bounds = int(np.sum((y_coords >= y1 - margin) & (y_coords <= y2 + margin)))
+
+    threshold = max(int(0.9 * n), n - 5)
+    assert x_in_bounds >= threshold, f'Only {x_in_bounds}/{n} x-coordinates within bounds'
+    assert y_in_bounds >= threshold, f'Only {y_in_bounds}/{n} y-coordinates within bounds'
+
+
+def test_different_bbox_sizes(pipnet_model, mock_image):
+    n = _expected_n_lms(pipnet_model)
+    test_bboxes = [
+        [50, 50, 150, 150],
+        [100, 100, 300, 300],
+        [50, 50, 400, 400],
+    ]
+
+    for bbox in test_bboxes:
+        landmarks = pipnet_model.get_landmarks(mock_image, bbox)
+        assert landmarks.shape == (n, 2), f'Failed for bbox {bbox}'
+
+
+def test_consistency(pipnet_model, mock_image, mock_bbox):
+    landmarks1 = pipnet_model.get_landmarks(mock_image, mock_bbox)
+    landmarks2 = pipnet_model.get_landmarks(mock_image, mock_bbox)
+    assert np.allclose(landmarks1, landmarks2), 'Same input should produce same landmarks'
+
+
+def test_different_image_sizes(pipnet_model, mock_bbox):
+    n = _expected_n_lms(pipnet_model)
+    test_sizes = [(480, 640, 3), (720, 1280, 3), (1080, 1920, 3)]
+
+    for size in test_sizes:
+        mock_image = np.random.randint(0, 255, size, dtype=np.uint8)
+        landmarks = pipnet_model.get_landmarks(mock_image, mock_bbox)
+        assert landmarks.shape == (n, 2), f'Failed for image size {size}'
+
+
+def test_bbox_list_format(pipnet_model, mock_image):
+    n = _expected_n_lms(pipnet_model)
+    landmarks = pipnet_model.get_landmarks(mock_image, [100, 100, 300, 300])
+    assert landmarks.shape == (n, 2), 'Should work with bbox as list'
+
+
+def test_bbox_array_format(pipnet_model, mock_image):
+    n = _expected_n_lms(pipnet_model)
+    bbox_array = np.array([100, 100, 300, 300])
+    landmarks = pipnet_model.get_landmarks(mock_image, bbox_array)
+    assert landmarks.shape == (n, 2), 'Should work with bbox as numpy array'
+
+
+def test_landmark_distribution(pipnet_model, mock_image, mock_bbox):
+    landmarks = pipnet_model.get_landmarks(mock_image, mock_bbox)
+
+    x_variance = np.var(landmarks[:, 0])
+    y_variance = np.var(landmarks[:, 1])
+
+    assert x_variance > 0, 'Landmarks should have variation in x-coordinates'
+    assert y_variance > 0, 'Landmarks should have variation in y-coordinates'
+
+
+def test_default_model_is_wflw_98():
+    """PIPNet() with no args should default to the 98-point WFLW model."""
+    model = PIPNet()
+    assert model.num_lms == 98
+
+
+def test_meanface_lookup_invalid_num_lms():
+    """get_meanface_info should reject unsupported landmark counts."""
+    from uniface.landmark._meanface import get_meanface_info
+
+    with pytest.raises(ValueError, match='No meanface table'):
+        get_meanface_info(num_lms=42)

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():
     """
@@ -176,6 +182,45 @@ def test_sphereface_normalized_embedding(sphereface_model, mock_landmarks):
     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/download_model.py

@@ -1,9 +1,11 @@
 import argparse
 
 from uniface.constants import (
+    AdaFaceWeights,
     AgeGenderWeights,
     ArcFaceWeights,
     DDAMFNWeights,
+    EdgeFaceWeights,
     HeadPoseWeights,
     LandmarkWeights,
     MobileFaceWeights,
@@ -15,9 +17,11 @@ 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,

tools/parse.py

@@ -198,7 +198,10 @@ def main():
     parser_arg.add_argument('--source', type=str, required=True, help='Image/video path or camera ID (0, 1, ...)')
     parser_arg.add_argument('--save-dir', type=str, default='outputs', help='Output directory')
     parser_arg.add_argument(
-        '--model', type=str, default=ParsingWeights.RESNET18, choices=[ParsingWeights.RESNET18, ParsingWeights.RESNET34]
+        '--model',
+        type=ParsingWeights,
+        default=ParsingWeights.RESNET18,
+        choices=list(ParsingWeights),
     )
     parser_arg.add_argument(
         '--expand-ratio',

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):
@@ -91,7 +97,7 @@ def main():
         '--recognizer',
         type=str,
         default='arcface',
-        choices=['arcface', 'mobileface', 'sphereface'],
+        choices=list(RECOGNIZERS),
     )
     args = parser.parse_args()
 

uniface/__init__.py

@@ -15,10 +15,11 @@
 
 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)
+- Facial landmarks (106 / 98 / 68-point detection: 2d106det, PIPNet)
 - Face parsing (semantic segmentation)
+- Portrait matting (trimap-free alpha matte)
 - Gaze estimation
 - Head pose estimation
 - Age, gender, and emotion prediction
@@ -30,7 +31,7 @@ from __future__ import annotations
 
 __license__ = 'MIT'
 __author__ = 'Yakhyokhuja Valikhujaev'
-__version__ = '3.3.0'
+__version__ = '3.6.0'
 
 import contextlib
 
@@ -50,10 +51,11 @@ from .detection import (
 )
 from .gaze import MobileGaze, create_gaze_estimator
 from .headpose import HeadPose, create_head_pose_estimator
-from .landmark import Landmark106, create_landmarker
+from .landmark import Landmark106, PIPNet, 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, HeadPoseResult, SpoofingResult
@@ -74,6 +76,7 @@ __all__ = [
     'create_detector',
     'create_face_parser',
     'create_gaze_estimator',
+    'create_matting_model',
     'create_head_pose_estimator',
     'create_landmarker',
     'create_recognizer',
@@ -87,16 +90,20 @@ __all__ = [
     # Recognition models
     'AdaFace',
     'ArcFace',
+    'EdgeFace',
     'MobileFace',
     'SphereFace',
     # Landmark models
     'Landmark106',
+    'PIPNet',
     # Gaze models
     'GazeResult',
     'MobileGaze',
     # Head pose models
     'HeadPose',
     'HeadPoseResult',
+    # Matting models
+    'MODNet',
     # Parsing models
     'BiSeNet',
     'XSeg',

uniface/analyzer.py

@@ -113,9 +113,10 @@ class FaceAnalyzer:
         return faces
 
     def __repr__(self) -> str:
-        parts = [f'FaceAnalyzer(detector={self.detector.__class__.__name__}']
+        parts = [f'detector={self.detector.__class__.__name__}']
         if self.recognizer:
             parts.append(f'recognizer={self.recognizer.__class__.__name__}')
-        for attr in self.attributes:
-            parts.append(f'{attr.__class__.__name__}')
-        return ', '.join(parts) + ')'
+        if self.attributes:
+            attr_names = ', '.join(attr.__class__.__name__ for attr in self.attributes)
+            parts.append(f'attributes=[{attr_names}]')
+        return f'FaceAnalyzer({", ".join(parts)})'

uniface/attribute/fairface.py

@@ -7,6 +7,7 @@ import cv2
 import numpy as np
 
 from uniface.attribute.base import Attribute
+from uniface.common import softmax
 from uniface.constants import FairFaceWeights
 from uniface.log import Logger
 from uniface.model_store import verify_model_weights
@@ -150,9 +151,9 @@ class FairFace(Attribute):
         race_logits, gender_logits, age_logits = prediction
 
         # Apply softmax
-        race_probs = self._softmax(race_logits[0])
-        gender_probs = self._softmax(gender_logits[0])
-        age_probs = self._softmax(age_logits[0])
+        race_probs = softmax(race_logits[0])
+        gender_probs = softmax(gender_logits[0])
+        age_probs = softmax(age_logits[0])
 
         # Get predictions
         race_idx = int(np.argmax(race_probs))
@@ -186,9 +187,3 @@ class FairFace(Attribute):
         face.age_group = result.age_group
         face.race = result.race
         return result
-
-    @staticmethod
-    def _softmax(x: np.ndarray) -> np.ndarray:
-        """Compute softmax values for numerical stability."""
-        exp_x = np.exp(x - np.max(x))
-        return exp_x / np.sum(exp_x)

uniface/common.py

@@ -19,6 +19,7 @@ __all__ = [
     'letterbox_resize',
     'non_max_suppression',
     'resize_image',
+    'softmax',
     'xyxy_to_cxcywh',
 ]
 
@@ -63,6 +64,21 @@ def resize_image(
     return image, resize_factor
 
 
+def softmax(x: np.ndarray, axis: int = -1) -> np.ndarray:
+    """Compute the numerically stable softmax of an array along ``axis``.
+
+    Args:
+        x: Input array.
+        axis: Axis along which softmax is computed. Defaults to the last axis.
+
+    Returns:
+        Array of the same shape as *x* with values in ``[0, 1]`` summing to 1
+        along *axis*.
+    """
+    exp_x = np.exp(x - np.max(x, axis=axis, keepdims=True))
+    return exp_x / np.sum(exp_x, axis=axis, keepdims=True)
+
+
 def xyxy_to_cxcywh(bboxes: np.ndarray) -> np.ndarray:
     """Convert bounding boxes from ``[x1, y1, x2, y2]`` to ``[cx, cy, w, 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.
@@ -143,6 +155,16 @@ class LandmarkWeights(str, Enum):
     DEFAULT = "2d_106"
 
 
+class PIPNetWeights(str, Enum):
+    """
+    PIPNet: Pixel-in-Pixel Net for facial landmark detection.
+    ResNet-18 backbone, 256x256 input.
+    https://github.com/yakhyo/pipnet-onnx
+    """
+    WFLW_98         = "pipnet_r18_wflw_98"
+    DW300_CELEBA_68 = "pipnet_r18_300w_celeba_68"
+
+
 class GazeWeights(str, Enum):
     """
     MobileGaze: Real-Time Gaze Estimation models.
@@ -189,6 +211,15 @@ 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.
@@ -278,6 +309,24 @@ 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',
@@ -340,6 +389,16 @@ MODEL_REGISTRY: dict[Enum, ModelInfo] = {
         sha256='f001b856447c413801ef5c42091ed0cd516fcd21f2d6b79635b1e733a7109dbf'
     ),
 
+    # PIPNet (98 / 68 point landmarks)
+    PIPNetWeights.WFLW_98: ModelInfo(
+        url='https://github.com/yakhyo/pipnet-onnx/releases/download/weights/pipnet_r18_wflw_98.onnx',
+        sha256='9862838dc6144bc772b6485f6f6d31295c0b1c1ab7293e6ddeb0a439cb10218d'
+    ),
+    PIPNetWeights.DW300_CELEBA_68: ModelInfo(
+        url='https://github.com/yakhyo/pipnet-onnx/releases/download/weights/pipnet_r18_300w_celeba_68.onnx',
+        sha256='63fa56fd4b8f6ccc4b88f2b36e00fa3d8c21a2c4244ab9381e8b432cef35197b'
+    ),
+
     # Gaze (MobileGaze)
     GazeWeights.RESNET18: ModelInfo(
         url='https://github.com/yakhyo/gaze-estimation/releases/download/weights/resnet18_gaze.onnx',
@@ -413,6 +472,16 @@ 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'
+    ),
 }
 
 
@@ -420,4 +489,5 @@ MODEL_REGISTRY: dict[Enum, ModelInfo] = {
 MODEL_URLS: dict[Enum, str] = {k: v.url for k, v in MODEL_REGISTRY.items()}
 MODEL_SHA256: dict[Enum, str] = {k: v.sha256 for k, v in MODEL_REGISTRY.items()}
 
-CHUNK_SIZE = 8192
+DOWNLOAD_CHUNK_SIZE = 256 * 1024  # 256 KiB
+HASH_CHUNK_SIZE = 1024 * 1024  # 1 MiB

uniface/detection/retinaface.py

@@ -202,7 +202,7 @@ class RetinaFace(BaseDetector):
         height, width, _ = image.shape
         image_tensor = self.preprocess(image)
 
-        # ONNXRuntime inference
+        # Inference
         outputs = self.inference(image_tensor)
 
         # Postprocessing

uniface/detection/scrfd.py

@@ -247,9 +247,10 @@ class SCRFD(BaseDetector):
 
         image_tensor = self.preprocess(image)
 
-        # ONNXRuntime inference
+        # Inference
         outputs = self.inference(image_tensor)
 
+        # Postprocessing
         scores_list, bboxes_list, kpss_list = self.postprocess(outputs, image_size=image.shape[:2])
 
         # Handle case when no faces are detected

uniface/detection/yolov8.py

@@ -2,18 +2,11 @@
 # Author: Yakhyokhuja Valikhujaev
 # GitHub: https://github.com/yakhyo
 
-"""
-YOLOv8-Face detector implementation.
-
-Uses anchor-free design with DFL (Distribution Focal Loss) for bbox regression.
-Reference: https://github.com/yakhyo/yolov8-face-onnx-inference
-"""
-
 from typing import Any, Literal
 
 import numpy as np
 
-from uniface.common import letterbox_resize, non_max_suppression
+from uniface.common import letterbox_resize, non_max_suppression, softmax
 from uniface.constants import YOLOv8FaceWeights
 from uniface.log import Logger
 from uniface.model_store import verify_model_weights
@@ -171,12 +164,6 @@ class YOLOv8Face(BaseDetector):
         """
         return self.session.run(self.output_names, {self.input_names: input_tensor})
 
-    @staticmethod
-    def _softmax(x: np.ndarray, axis: int = -1) -> np.ndarray:
-        """Compute softmax values for array x along specified axis."""
-        exp_x = np.exp(x - np.max(x, axis=axis, keepdims=True))
-        return exp_x / np.sum(exp_x, axis=axis, keepdims=True)
-
     def postprocess(
         self,
         predictions: list[np.ndarray],
@@ -224,7 +211,7 @@ class YOLOv8Face(BaseDetector):
 
             # Decode bounding boxes from DFL
             bbox_pred = bbox_pred.reshape(-1, 4, 16)
-            bbox_dist = self._softmax(bbox_pred, axis=-1) @ np.arange(16)
+            bbox_dist = softmax(bbox_pred, axis=-1) @ np.arange(16)
 
             # Convert distances to xyxy format
             x1 = (grid_x - bbox_dist[:, 0]) * stride
@@ -279,16 +266,14 @@ class YOLOv8Face(BaseDetector):
         if len(keep) == 0:
             return np.array([]), np.array([])
 
-        # Limit to max_det
+        # Filter detections and limit to max_det
         keep = keep[: self.max_det]
         boxes = boxes[keep]
         scores = scores[keep]
         landmarks = landmarks[keep]
 
-        # === SCALE TO ORIGINAL IMAGE COORDINATES ===
+        # Scale back to original image coordinates
         pad_w, pad_h = padding
-
-        # Scale boxes back to original image coordinates
         boxes[:, [0, 2]] = (boxes[:, [0, 2]] - pad_w) / scale
         boxes[:, [1, 3]] = (boxes[:, [1, 3]] - pad_h) / scale
 
@@ -303,7 +288,7 @@ class YOLOv8Face(BaseDetector):
         # Reshape landmarks to (N, 5, 2)
         landmarks = landmarks.reshape(-1, 5, 2)
 
-        # Combine box and score
+        # Combine results
         detections = np.concatenate([boxes, scores[:, None]], axis=1)
 
         return detections, landmarks

uniface/draw.py

@@ -677,14 +677,10 @@ def vis_parsing_maps(
     segmentation_mask = segmentation_mask.copy().astype(np.uint8)
 
     # Create a color mask
-    segmentation_mask_color = np.zeros((segmentation_mask.shape[0], segmentation_mask.shape[1], 3))
-
-    num_classes = np.max(segmentation_mask)
-    for class_index in range(1, num_classes + 1):
-        class_pixels = np.where(segmentation_mask == class_index)
-        segmentation_mask_color[class_pixels[0], class_pixels[1], :] = FACE_PARSING_COLORS[class_index]
-
-    segmentation_mask_color = segmentation_mask_color.astype(np.uint8)
+    max_class = int(segmentation_mask.max())
+    palette = np.zeros((max(max_class + 1, len(FACE_PARSING_COLORS)), 3), dtype=np.uint8)
+    palette[: len(FACE_PARSING_COLORS)] = FACE_PARSING_COLORS
+    segmentation_mask_color = palette[segmentation_mask]
 
     # Convert image to BGR format for blending
     bgr_image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)

uniface/gaze/models.py

@@ -6,6 +6,7 @@
 import cv2
 import numpy as np
 
+from uniface.common import softmax
 from uniface.constants import GazeWeights
 from uniface.log import Logger
 from uniface.model_store import verify_model_weights
@@ -142,11 +143,6 @@ class MobileGaze(BaseGazeEstimator):
 
         return image
 
-    def _softmax(self, x: np.ndarray) -> np.ndarray:
-        """Apply softmax along axis 1."""
-        e_x = np.exp(x - np.max(x, axis=1, keepdims=True))
-        return e_x / e_x.sum(axis=1, keepdims=True)
-
     def postprocess(self, outputs: tuple[np.ndarray, np.ndarray]) -> GazeResult:
         """
         Postprocess raw model outputs into gaze angles.
@@ -164,8 +160,8 @@ class MobileGaze(BaseGazeEstimator):
         yaw_logits, pitch_logits = outputs
 
         # Convert logits to probabilities
-        yaw_probs = self._softmax(yaw_logits)
-        pitch_probs = self._softmax(pitch_logits)
+        yaw_probs = softmax(yaw_logits)
+        pitch_probs = softmax(pitch_logits)
 
         # Compute expected bin index (soft-argmax)
         yaw_deg = np.sum(yaw_probs * self._idx_tensor, axis=1) * self._binwidth - self._angle_offset
@@ -183,6 +179,13 @@ class MobileGaze(BaseGazeEstimator):
 
         This method orchestrates the full pipeline: preprocessing the input,
         running inference, and postprocessing to return the gaze direction.
+
+        Args:
+            face_image (np.ndarray): A cropped face image in BGR format with shape (H, W, 3).
+
+        Returns:
+            GazeResult: Estimated gaze direction containing ``pitch`` (vertical) and
+                ``yaw`` (horizontal) angles in radians.
         """
         input_tensor = self.preprocess(face_image)
         outputs = self.session.run(self.output_names, {self.input_name: input_tensor})

uniface/headpose/models.py

@@ -170,6 +170,13 @@ class HeadPose(BaseHeadPoseEstimator):
 
         This method orchestrates the full pipeline: preprocessing the input,
         running inference, and postprocessing to return the head orientation.
+
+        Args:
+            face_image (np.ndarray): A cropped face image in BGR format with shape (H, W, 3).
+
+        Returns:
+            HeadPoseResult: Estimated head orientation containing ``pitch`` (vertical),
+                ``yaw`` (horizontal), and ``roll`` (in-plane) angles in degrees.
         """
         input_tensor = self.preprocess(face_image)
         outputs = self.session.run(self.output_names, {self.input_name: input_tensor})

uniface/landmark/__init__.py

@@ -4,26 +4,35 @@
 
 from .base import BaseLandmarker
 from .models import Landmark106
+from .pipnet import PIPNet
 
 
 def create_landmarker(method: str = '2d106det', **kwargs) -> BaseLandmarker:
-    """
-    Factory function to create facial landmark predictors.
+    """Factory function to create facial landmark predictors.
 
     Args:
         method (str): Landmark prediction method.
-            Options: '2d106det' (default), 'landmark106', '106'.
-        **kwargs: Model-specific parameters.
+            Options:
+                - ``'2d106det'`` (default): InsightFace 2d106det 106-point model.
+                - ``'pipnet'``: PIPNet 98-point (WFLW) or 68-point (300W+CelebA)
+                  model. Pass ``model_name=PIPNetWeights.DW300_CELEBA_68`` for
+                  the 68-point variant.
+        **kwargs: Model-specific parameters forwarded to the underlying class.
 
     Returns:
         Initialized landmarker instance.
+
+    Raises:
+        ValueError: If ``method`` is not supported.
     """
     method = method.lower()
-    if method in ('2d106det', 'landmark106', '106'):
+    if method == '2d106det':
         return Landmark106(**kwargs)
-    else:
-        available = ['2d106det', 'landmark106', '106']
-        raise ValueError(f"Unsupported method: '{method}'. Available: {available}")
+    if method == 'pipnet':
+        return PIPNet(**kwargs)
+
+    available = ['2d106det', 'pipnet']
+    raise ValueError(f"Unsupported method: '{method}'. Available: {available}")
 
 
-__all__ = ['BaseLandmarker', 'Landmark106', 'create_landmarker']
+__all__ = ['BaseLandmarker', 'Landmark106', 'PIPNet', 'create_landmarker']

uniface/landmark/_meanface.py

@@ -0,0 +1,161 @@
+# Copyright 2025-2026 Yakhyokhuja Valikhujaev
+# Author: Yakhyokhuja Valikhujaev
+# GitHub: https://github.com/yakhyo
+#
+# Mean-face arrays vendored from upstream PIPNet (MIT):
+#   https://github.com/jhb86253817/PIPNet/tree/master/data
+
+from __future__ import annotations
+
+import numpy as np
+
+# fmt: off
+# 300W layout: 68 landmarks, 136 floats.
+MEANFACE_300W_68: tuple[float, ...] = (
+    0.05558998895410058, 0.23848280098218655, 0.05894856684324656, 0.3590187767402909,
+    0.0736574254414371, 0.4792196439871159, 0.09980016420365162, 0.5959029676167197,
+    0.14678670154995865, 0.7035615597409001, 0.21847188218752928, 0.7971705893013413,
+    0.30554692814599393, 0.8750572978073209, 0.4018434142644611, 0.9365018059444535,
+    0.5100536090382116, 0.9521295666029498, 0.6162039414413925, 0.9309467340899419,
+    0.7094522484942942, 0.8669275031738761, 0.7940993502957612, 0.7879369615524398,
+    0.8627063649669019, 0.6933756633633967, 0.9072386130534111, 0.5836975017700834,
+    0.9298874997796132, 0.4657004930314701, 0.9405202670724796, 0.346063993805527,
+    0.9425419553088846, 0.22558131891345742, 0.13304298285530403, 0.14853071838028062,
+    0.18873587368440375, 0.09596491613770254, 0.2673231915839219, 0.08084218279128136,
+    0.34878638553224905, 0.09253591849498964, 0.4226713753717798, 0.12466063383809506,
+    0.5618513152452376, 0.11839668911898667, 0.6394952560845826, 0.08480191391770678,
+    0.7204375851516752, 0.07249669092117161, 0.7988615904537885, 0.08766933146893043,
+    0.8534884939460948, 0.1380096813348583, 0.49610677423740546, 0.21516740699375395,
+    0.49709661403980665, 0.2928875699060973, 0.4982292618461611, 0.3699985379939941,
+    0.49982965173254235, 0.4494119144493957, 0.406772397599095, 0.5032397294041786,
+    0.45231994786363067, 0.5197953144002292, 0.49969685987914064, 0.5332489262413073,
+    0.5470074224053442, 0.518413595827126, 0.5892261151542287, 0.5023530079850803,
+    0.22414578747180394, 0.22835847349949062, 0.27262947128194215, 0.19915251892241678,
+    0.3306759252861797, 0.20026034220607236, 0.38044435864341913, 0.23839196034290633,
+    0.32884072789429913, 0.24902443794896897, 0.2707409300714473, 0.24950886025380967,
+    0.6086826011068529, 0.23465048639345917, 0.660397116846103, 0.1937087938594717,
+    0.7177815187666494, 0.19317079039835858, 0.7652328176062365, 0.22088822845258235,
+    0.722727677909097, 0.24195514178450958, 0.6658378927310327, 0.2441554205021945,
+    0.32894370935769124, 0.6496589505331646, 0.39347179739100613, 0.6216899667490776,
+    0.4571976492475472, 0.60794251109236, 0.4990484623797022, 0.6190124015360254,
+    0.5465555522325872, 0.6071477960565326, 0.6116127327356168, 0.6205387097430033,
+    0.6742318496058836, 0.6437466364395467, 0.6144773141699744, 0.7077526646009754,
+    0.5526442055374252, 0.7363350735898412, 0.5018120662554302, 0.7424476622366345,
+    0.4554458875556401, 0.7382303858617719, 0.3923750731597415, 0.7118887028663435,
+    0.35530766372404593, 0.6524479416354049, 0.457111071610868, 0.6467108367268608,
+    0.49974082228815025, 0.6508406774477011, 0.5477027224368399, 0.6451242819422733,
+    0.6478392760505715, 0.647852382880368, 0.5488474760115958, 0.6779061893042735,
+    0.5001073351044452, 0.6845280260362221, 0.4564831746654594, 0.6799300301441035,
+)
+
+# WFLW layout: 98 landmarks, 196 floats.
+MEANFACE_WFLW_98: tuple[float, ...] = (
+    0.07960419395480703, 0.3921576875344978, 0.08315055593117261, 0.43509551571809146,
+    0.08675705281580391, 0.47810288286566444, 0.09141892980469117, 0.5210356946467262,
+    0.09839925903528965, 0.5637522280060038, 0.10871037524559955, 0.6060410614977951,
+    0.12314562992759207, 0.6475338700558225, 0.14242389255404694, 0.6877152027028081,
+    0.16706295456951875, 0.7259564546408682, 0.19693946055282413, 0.761730578566735,
+    0.23131827931527224, 0.7948205670466106, 0.2691730934906831, 0.825332081636482,
+    0.3099415030959131, 0.853325959406618, 0.3535202097901413, 0.8782538906229107,
+    0.40089023799272033, 0.8984102434399625, 0.4529251732310723, 0.9112191359814178,
+    0.5078640056794708, 0.9146712690731943, 0.5616519666079889, 0.9094327772020283,
+    0.6119216923689698, 0.8950540037623425, 0.6574617882337107, 0.8738084866764846,
+    0.6994820494908942, 0.8482660530943744, 0.7388135339780575, 0.8198750461527688,
+    0.775158750479601, 0.788989141243473, 0.8078785221990765, 0.7555462713420953,
+    0.8361052138935441, 0.7195542055115057, 0.8592123871172533, 0.6812759034843933,
+    0.8771159986952748, 0.6412243940605555, 0.8902481006481506, 0.5999743595282084,
+    0.8992952868651163, 0.5580032282594118, 0.9050110573289222, 0.5156548913779377,
+    0.908338439928252, 0.4731336721500472, 0.9104896075281127, 0.4305382486815422,
+    0.9124796341441906, 0.38798192678294363, 0.18465941635742913, 0.35063191749632183,
+    0.24110421889338157, 0.31190394310826886, 0.3003235400132397, 0.30828189837331976,
+    0.3603094923651325, 0.3135606490643205, 0.4171060234289877, 0.32433417646045615,
+    0.416842139562573, 0.3526729965541497, 0.36011177591813404, 0.3439660526998693,
+    0.3000863121140166, 0.33890077494044946, 0.24116055928407834, 0.34065620413845005,
+    0.5709736930161899, 0.321407825750195, 0.6305694459247149, 0.30972642336729495,
+    0.6895161625920927, 0.3036453838462943, 0.7488591859761683, 0.3069143844433495,
+    0.8030471337135181, 0.3435156012309415, 0.7485083446528741, 0.3348759588212388,
+    0.6893025057931884, 0.33403402013776456, 0.6304822892126991, 0.34038458762875695,
+    0.5710009285609654, 0.34988479902594455, 0.4954171902473609, 0.40202330022004634,
+    0.49604903449415433, 0.4592869389138444, 0.49644391662771625, 0.5162862508677217,
+    0.4981161256057368, 0.5703284628419502, 0.40749001573145566, 0.5983629921847019,
+    0.4537396729649631, 0.6057169923583451, 0.5007345777827058, 0.6116695615531077,
+    0.5448481727980428, 0.6044131443745976, 0.5882140504891681, 0.5961738788380111,
+    0.24303324896316683, 0.40721003719912746, 0.27771706732644313, 0.3907171413930685,
+    0.31847706697401107, 0.38417234007271117, 0.3621792860449715, 0.3900847721320633,
+    0.3965299162804086, 0.41071434661355205, 0.3586805562211872, 0.4203724421417311,
+    0.31847860588240934, 0.4237674602252073, 0.2789458001651631, 0.41942757306509065,
+    0.5938514626567266, 0.4090628827047304, 0.6303565516542536, 0.3864501652756091,
+    0.6774844732813035, 0.3809319896905685, 0.7150854850525555, 0.3875173254527522,
+    0.747519807465081, 0.4025187328459307, 0.7155172856447009, 0.4145958479293519,
+    0.680051949453018, 0.420041513473271, 0.6359056750107122, 0.41803782782566573,
+    0.33916483987223056, 0.6968581311227738, 0.40008790639758807, 0.6758101185779204,
+    0.47181947887764153, 0.6678850445191217, 0.5025394453374782, 0.6682917934792593,
+    0.5337748367911458, 0.6671949030019636, 0.6015915330083903, 0.6742535357237751,
+    0.6587068892667173, 0.6932163943648724, 0.6192795131720007, 0.7283129162844936,
+    0.5665923267827963, 0.7550248076404299, 0.5031303335863617, 0.7648348885181623,
+    0.4371030429958871, 0.7572539606688756, 0.3814909500115824, 0.7320595346122074,
+    0.35129809553480984, 0.6986839074746692, 0.4247987356100664, 0.69127609583798,
+    0.5027677238758598, 0.6911145821740593, 0.576997542122097, 0.6896269708051024,
+    0.6471352843446794, 0.6948977432227927, 0.5799932528781817, 0.7185288017567538,
+    0.5024914756021335, 0.7285408331555782, 0.4218115644247556, 0.7209126133193829,
+    0.3219750495122499, 0.40376441481225156, 0.6751136343101699, 0.40023415216110797,
+)
+# fmt: on
+
+
+def _build_neighbor_indices(meanface: np.ndarray, num_nb: int) -> tuple[list[int], list[int], int]:
+    num_lms = meanface.shape[0]
+    meanface_indices: list[list[int]] = []
+    for i in range(num_lms):
+        pt = meanface[i]
+        dists = np.sum((pt - meanface) ** 2, axis=1)
+        indices = np.argsort(dists)
+        meanface_indices.append(indices[1 : 1 + num_nb].tolist())
+
+    reversed_map: dict[int, tuple[list[int], list[int]]] = {i: ([], []) for i in range(num_lms)}
+    for i in range(num_lms):
+        for j in range(num_nb):
+            neighbor = meanface_indices[i][j]
+            reversed_map[neighbor][0].append(i)
+            reversed_map[neighbor][1].append(j)
+
+    max_len = max(len(reversed_map[i][0]) for i in range(num_lms))
+
+    reverse_index1: list[int] = []
+    reverse_index2: list[int] = []
+    for i in range(num_lms):
+        idx1, idx2 = reversed_map[i]
+        # Pad by repeating entries so every landmark has the same neighbor count.
+        pad1 = (idx1 * max_len)[: max_len - len(idx1)]
+        pad2 = (idx2 * max_len)[: max_len - len(idx2)]
+        reverse_index1.extend(idx1 + pad1)
+        reverse_index2.extend(idx2 + pad2)
+
+    return reverse_index1, reverse_index2, max_len
+
+
+def get_meanface_info(num_lms: int, num_nb: int = 10) -> tuple[np.ndarray, np.ndarray, int]:
+    """Precomputed reverse-index tables for PIPNet decoding.
+
+    Args:
+        num_lms: 68 (300W) or 98 (WFLW).
+        num_nb: Neighbor count used at training time.
+
+    Returns:
+        ``(reverse_index1, reverse_index2, max_len)``.
+
+    Raises:
+        ValueError: If ``num_lms`` does not match a shipped meanface table.
+    """
+    if num_lms == 68:
+        flat = MEANFACE_300W_68
+    elif num_lms == 98:
+        flat = MEANFACE_WFLW_98
+    else:
+        raise ValueError(f'No meanface table available for num_lms={num_lms}; expected 68 or 98.')
+
+    meanface = np.asarray(flat, dtype=np.float32).reshape(-1, 2)
+    assert meanface.shape[0] == num_lms, f'meanface mismatch: expected {num_lms} points, got {meanface.shape[0]}'
+
+    r1, r2, max_len = _build_neighbor_indices(meanface, num_nb)
+    return np.asarray(r1, dtype=np.int64), np.asarray(r2, dtype=np.int64), max_len

uniface/landmark/models.py

@@ -58,7 +58,7 @@ class Landmark106(BaseLandmarker):
         self.model_path = verify_model_weights(model_name)
         self._initialize_model()
 
-    def _initialize_model(self):
+    def _initialize_model(self) -> None:
         """
         Initialize the ONNX model from the stored model path.
 

uniface/landmark/pipnet.py

@@ -0,0 +1,240 @@
+# Copyright 2025-2026 Yakhyokhuja Valikhujaev
+# Author: Yakhyokhuja Valikhujaev
+# GitHub: https://github.com/yakhyo
+
+
+from __future__ import annotations
+
+import cv2
+import numpy as np
+
+from uniface.constants import PIPNetWeights
+from uniface.log import Logger
+from uniface.model_store import verify_model_weights
+from uniface.onnx_utils import create_onnx_session
+
+from ._meanface import get_meanface_info
+from .base import BaseLandmarker
+
+__all__ = ['PIPNet']
+
+# PIPNet's upstream training preprocessing pads the bbox asymmetrically:
+# +10% on the left, right, and bottom and -10% on the top before cropping.
+_BBOX_PAD_RATIO = 0.1
+
+
+class PIPNet(BaseLandmarker):
+    """PIPNet facial landmark detector (98 or 68 points).
+
+    PIPNet (Pixel-in-Pixel Net) detects landmarks via a heatmap classification
+    head plus per-pixel offset and neighbor regression heads. The neighbor
+    predictions are gathered through a reverse-index table built from a
+    pre-trained meanface and then averaged with each landmark's own
+    prediction for sub-pixel accuracy.
+
+    Both the WFLW (98 points) and 300W+CelebA (68 points) variants share the
+    same ResNet-18 backbone and 256x256 input. The number of landmarks is
+    inferred from the ONNX output shape and the corresponding meanface table
+    is selected automatically.
+
+    Args:
+        model_name (PIPNetWeights): Which PIPNet ONNX model to load.
+            Defaults to ``PIPNetWeights.WFLW_98``.
+        providers (list[str] | None): ONNX Runtime execution providers. If None,
+            auto-detects the best available provider.
+
+    Example:
+        >>> from uniface.landmark import PIPNet
+        >>> from uniface.constants import PIPNetWeights
+        >>>
+        >>> landmarker = PIPNet()  # WFLW_98 by default
+        >>> landmarks = landmarker.get_landmarks(image, bbox)
+        >>> print(landmarks.shape)
+        (98, 2)
+        >>>
+        >>> landmarker_68 = PIPNet(model_name=PIPNetWeights.DW300_CELEBA_68)
+        >>> landmarks_68 = landmarker_68.get_landmarks(image, bbox)
+        >>> print(landmarks_68.shape)
+        (68, 2)
+    """
+
+    def __init__(
+        self,
+        model_name: PIPNetWeights = PIPNetWeights.WFLW_98,
+        providers: list[str] | None = None,
+    ) -> None:
+        Logger.info(f'Initializing PIPNet with model={model_name}')
+        self.providers = providers
+
+        self.input_mean = np.array([0.485, 0.456, 0.406], dtype=np.float32)
+        self.input_std = np.array([0.229, 0.224, 0.225], dtype=np.float32)
+
+        # Number of meanface neighbors used at training time.
+        self.num_neighbors = 10
+
+        self.model_path = verify_model_weights(model_name)
+        self._initialize_model()
+
+    def _initialize_model(self) -> None:
+        """Initialize the ONNX model and precompute the meanface tables.
+
+        Raises:
+            RuntimeError: If the model fails to load or initialize.
+            ValueError: If the model output channel count does not match a
+                supported meanface table (expected 68 or 98 landmarks).
+        """
+        try:
+            self.session = create_onnx_session(self.model_path, providers=self.providers)
+
+            input_meta = self.session.get_inputs()[0]
+            self.input_name = input_meta.name
+            _, _, self.input_h, self.input_w = input_meta.shape
+
+            outputs = self.session.get_outputs()
+            self.output_names = [o.name for o in outputs]
+
+            cls_shape = outputs[0].shape  # (1, num_lms, feat_h, feat_w)
+            self.num_lms = int(cls_shape[1])
+            self.feat_h = int(cls_shape[2]) if isinstance(cls_shape[2], int) else self.input_h // 32
+            self.feat_w = int(cls_shape[3]) if isinstance(cls_shape[3], int) else self.input_w // 32
+            self.net_stride = self.input_h // self.feat_h
+
+            self._reverse_index1, self._reverse_index2, self._max_len = get_meanface_info(
+                self.num_lms, self.num_neighbors
+            )
+
+            Logger.info(f'Model initialized with {self.num_lms} landmarks ({self.input_h}x{self.input_w} input)')
+
+        except ValueError:
+            raise
+        except Exception as e:
+            Logger.error(f"Failed to load PIPNet model from '{self.model_path}'", exc_info=True)
+            raise RuntimeError(f'Failed to initialize PIPNet model: {e}') from e
+
+    def preprocess(self, image: np.ndarray, bbox: np.ndarray) -> tuple[np.ndarray, tuple[int, int, int, int]]:
+        """Crop the face region and produce the network input blob.
+
+        The crop follows the upstream PIPNet convention: pad ``+10%`` on the
+        left, right, and bottom of the bbox and ``-10%`` on the top, then
+        clamp to the image bounds. The crop is resized to the model's input
+        resolution, BGR->RGB converted, and ImageNet-normalized.
+
+        Args:
+            image (np.ndarray): Full source image in BGR format, ``(H, W, 3)``.
+            bbox (np.ndarray): Face bounding box ``[x1, y1, x2, y2]``.
+
+        Returns:
+            Tuple of:
+                - The preprocessed ``(1, 3, H, W)`` float32 blob.
+                - The crop metadata ``(x1, y1, crop_w, crop_h)`` used to
+                  rescale predictions back to original image coordinates.
+        """
+        crop, crop_meta = self._crop_face(image, bbox)
+        resized = cv2.resize(crop, (self.input_w, self.input_h))
+        rgb = cv2.cvtColor(resized, cv2.COLOR_BGR2RGB).astype(np.float32) / 255.0
+        rgb = (rgb - self.input_mean) / self.input_std
+        blob = np.transpose(rgb, (2, 0, 1))[None, ...]
+        return np.ascontiguousarray(blob, dtype=np.float32), crop_meta
+
+    def _crop_face(self, image: np.ndarray, bbox: np.ndarray) -> tuple[np.ndarray, tuple[int, int, int, int]]:
+        img_h, img_w = image.shape[:2]
+        x1, y1, x2, y2 = (float(v) for v in bbox[:4])
+        det_w = x2 - x1 + 1
+        det_h = y2 - y1 + 1
+
+        # Asymmetric: +10% left/right/bottom, -10% top.
+        x1 -= int(det_w * _BBOX_PAD_RATIO)
+        y1 += int(det_h * _BBOX_PAD_RATIO)
+        x2 += int(det_w * _BBOX_PAD_RATIO)
+        y2 += int(det_h * _BBOX_PAD_RATIO)
+
+        x1 = max(int(x1), 0)
+        y1 = max(int(y1), 0)
+        x2 = min(int(x2), img_w - 1)
+        y2 = min(int(y2), img_h - 1)
+        crop_w = x2 - x1 + 1
+        crop_h = y2 - y1 + 1
+
+        crop = image[y1 : y2 + 1, x1 : x2 + 1, :]
+        return crop, (x1, y1, crop_w, crop_h)
+
+    def postprocess(
+        self,
+        outputs: tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray],
+        crop_meta: tuple[int, int, int, int],
+    ) -> np.ndarray:
+        """Decode raw network outputs into original-image landmark coordinates.
+
+        Combines each landmark's own (cls, offset) prediction with the
+        predictions made about it by its ``num_nb`` nearest meanface neighbors,
+        then maps the normalized result back to the original image using the
+        crop metadata.
+
+        Args:
+            outputs (tuple): The five raw ONNX outputs in order
+                ``(cls_map, offset_x, offset_y, nb_x, nb_y)``.
+            crop_meta (tuple): The ``(x1, y1, crop_w, crop_h)`` returned by
+                :meth:`preprocess`.
+
+        Returns:
+            np.ndarray: ``(num_lms, 2)`` float32 landmarks in original image space.
+        """
+        cls_map, offset_x, offset_y, nb_x, nb_y = outputs
+        n = self.num_lms
+        nb = self.num_neighbors
+        h = self.feat_h
+        w = self.feat_w
+
+        cls_flat = cls_map.reshape(n, h * w)
+        max_ids = np.argmax(cls_flat, axis=1)
+        cols = (max_ids % w).astype(np.float32)
+        rows = (max_ids // w).astype(np.float32)
+
+        off_x_flat = offset_x.reshape(n, h * w)
+        off_y_flat = offset_y.reshape(n, h * w)
+        own_x = np.take_along_axis(off_x_flat, max_ids[:, None], axis=1).squeeze(1)
+        own_y = np.take_along_axis(off_y_flat, max_ids[:, None], axis=1).squeeze(1)
+
+        # Neighbor channels are ordered (num_nb * num_lms); reshape so axis 1 is neighbor index.
+        nb_x_flat = nb_x.reshape(n, nb, h * w)
+        nb_y_flat = nb_y.reshape(n, nb, h * w)
+        nb_ids = np.broadcast_to(max_ids[:, None, None], (n, nb, 1))
+        nb_own_x = np.take_along_axis(nb_x_flat, nb_ids, axis=2).squeeze(2)
+        nb_own_y = np.take_along_axis(nb_y_flat, nb_ids, axis=2).squeeze(2)
+
+        scale_x = self.input_w / self.net_stride
+        scale_y = self.input_h / self.net_stride
+        pred_x = (cols + own_x) / scale_x
+        pred_y = (rows + own_y) / scale_y
+
+        nb_pred_x = (cols[:, None] + nb_own_x) / scale_x
+        nb_pred_y = (rows[:, None] + nb_own_y) / scale_y
+
+        # Reverse gather: collect predictions about landmark i made by its neighbors,
+        # then average with landmark i's own prediction.
+        rev_x = nb_pred_x.reshape(-1)[self._reverse_index1 * nb + self._reverse_index2].reshape(n, self._max_len)
+        rev_y = nb_pred_y.reshape(-1)[self._reverse_index1 * nb + self._reverse_index2].reshape(n, self._max_len)
+
+        merged_x = np.mean(np.concatenate([pred_x[:, None], rev_x], axis=1), axis=1)
+        merged_y = np.mean(np.concatenate([pred_y[:, None], rev_y], axis=1), axis=1)
+
+        x1, y1, crop_w, crop_h = crop_meta
+        merged_x = merged_x * crop_w + x1
+        merged_y = merged_y * crop_h + y1
+
+        return np.stack([merged_x, merged_y], axis=1).astype(np.float32)
+
+    def get_landmarks(self, image: np.ndarray, bbox: np.ndarray) -> np.ndarray:
+        """Predict facial landmarks for the given face bounding box.
+
+        Args:
+            image (np.ndarray): Full source image in BGR format.
+            bbox (np.ndarray): Face bounding box ``[x1, y1, x2, y2]``.
+
+        Returns:
+            np.ndarray: Landmark points as a ``(num_lms, 2)`` float32 array
+                in the original image's pixel coordinates.
+        """
+        blob, crop_meta = self.preprocess(image, bbox)
+        outputs = self.session.run(self.output_names, {self.input_name: blob})
+        return self.postprocess(outputs, crop_meta)

uniface/log.py

@@ -2,12 +2,6 @@
 # Author: Yakhyokhuja Valikhujaev
 # GitHub: https://github.com/yakhyo
 
-"""Logging utilities for UniFace.
-
-This module provides a centralized logger for the UniFace library,
-allowing users to enable verbose logging when debugging or developing.
-"""
-
 from __future__ import annotations
 
 import logging

uniface/matting/__init__.py

@@ -0,0 +1,53 @@
+# Copyright 2025-2026 Yakhyokhuja Valikhujaev
+# Author: Yakhyokhuja Valikhujaev
+# GitHub: https://github.com/yakhyo
+
+from __future__ import annotations
+
+from uniface.constants import MODNetWeights
+
+from .base import BaseMatting
+from .modnet import MODNet
+
+__all__ = ['BaseMatting', 'MODNet', 'create_matting_model']
+
+
+def create_matting_model(
+    model_name: str | MODNetWeights = MODNetWeights.PHOTOGRAPHIC,
+    **kwargs,
+) -> BaseMatting:
+    """Factory function to create a portrait matting model.
+
+    Args:
+        model_name: Model to create. Options: ``MODNetWeights.PHOTOGRAPHIC``
+            (high-quality photos), ``MODNetWeights.WEBCAM`` (real-time webcam).
+            Also accepts string values like ``"modnet_photographic"`` or
+            ``"modnet_webcam"``.
+        **kwargs: Additional arguments passed to the model constructor
+            (e.g. ``input_size``, ``providers``).
+
+    Returns:
+        An instance of the requested matting model.
+
+    Raises:
+        ValueError: If the model_name is not recognized.
+
+    Example:
+        >>> matting = create_matting_model()
+        >>> matte = matting.predict(image)
+    """
+    if isinstance(model_name, MODNetWeights):
+        return MODNet(model_name=model_name, **kwargs)
+
+    if isinstance(model_name, str):
+        try:
+            weights = MODNetWeights(model_name)
+            return MODNet(model_name=weights, **kwargs)
+        except ValueError:
+            pass
+
+        valid_models = [m.value for m in MODNetWeights]
+        raise ValueError(f"Unknown matting model: '{model_name}'. Valid options are: {', '.join(valid_models)}")
+
+    valid_models = [m.value for m in MODNetWeights]
+    raise ValueError(f"Unknown matting model: '{model_name}'. Valid options are: {', '.join(valid_models)}")

uniface/matting/base.py

@@ -0,0 +1,88 @@
+# Copyright 2025-2026 Yakhyokhuja Valikhujaev
+# Author: Yakhyokhuja Valikhujaev
+# GitHub: https://github.com/yakhyo
+
+from __future__ import annotations
+
+from abc import ABC, abstractmethod
+
+import numpy as np
+
+
+class BaseMatting(ABC):
+    """Abstract base class for portrait matting models.
+
+    Unlike face parsers that operate on face crops and produce class labels or
+    face-region masks, matting models operate on full images and produce a soft
+    alpha matte (float32 in [0, 1]) separating foreground from background.
+
+    Subclasses must implement the full pipeline: model initialization,
+    preprocessing, postprocessing, and the ``predict`` entry point.
+    """
+
+    @abstractmethod
+    def _initialize_model(self) -> None:
+        """Initialize the underlying model for inference.
+
+        This method should handle loading model weights, creating the
+        inference session (e.g., ONNX Runtime), and any necessary
+        setup procedures to prepare the model for prediction.
+
+        Raises:
+            RuntimeError: If the model fails to load or initialize.
+        """
+        raise NotImplementedError('Subclasses must implement the _initialize_model method.')
+
+    @abstractmethod
+    def preprocess(self, image: np.ndarray) -> tuple[np.ndarray, int, int]:
+        """Preprocess the input image for model inference.
+
+        Args:
+            image: An image in BGR format with shape ``(H, W, 3)``.
+
+        Returns:
+            A tuple of ``(tensor, orig_h, orig_w)`` where *tensor* is the
+            preprocessed image ready for inference.
+        """
+        raise NotImplementedError('Subclasses must implement the preprocess method.')
+
+    @abstractmethod
+    def postprocess(self, outputs: np.ndarray, original_size: tuple[int, int]) -> np.ndarray:
+        """Postprocess raw model outputs into an alpha matte.
+
+        Args:
+            outputs: Raw outputs from the model inference.
+            original_size: Original image size as ``(width, height)``.
+
+        Returns:
+            Alpha matte with shape ``(H, W)`` and values in ``[0, 1]``.
+        """
+        raise NotImplementedError('Subclasses must implement the postprocess method.')
+
+    @abstractmethod
+    def predict(self, image: np.ndarray) -> np.ndarray:
+        """Run end-to-end matting on an image.
+
+        Args:
+            image: An image in BGR format with shape ``(H, W, 3)``.
+
+        Returns:
+            Alpha matte with shape ``(H, W)``, float32 in ``[0, 1]``.
+
+        Example:
+            >>> matting = create_matting_model()
+            >>> matte = matting.predict(image)
+            >>> print(f'Matte shape: {matte.shape}, dtype: {matte.dtype}')
+        """
+        raise NotImplementedError('Subclasses must implement the predict method.')
+
+    def __call__(self, image: np.ndarray) -> np.ndarray:
+        """Callable shortcut for :meth:`predict`.
+
+        Args:
+            image: An image in BGR format with shape ``(H, W, 3)``.
+
+        Returns:
+            Alpha matte with shape ``(H, W)``, float32 in ``[0, 1]``.
+        """
+        return self.predict(image)

uniface/matting/modnet.py

@@ -0,0 +1,162 @@
+# Copyright 2025-2026 Yakhyokhuja Valikhujaev
+# Author: Yakhyokhuja Valikhujaev
+# GitHub: https://github.com/yakhyo
+
+from __future__ import annotations
+
+import cv2
+import numpy as np
+
+from uniface.constants import MODNetWeights
+from uniface.log import Logger
+from uniface.model_store import verify_model_weights
+from uniface.onnx_utils import create_onnx_session
+
+from .base import BaseMatting
+
+__all__ = ['MODNet']
+
+STRIDE = 32
+
+
+class MODNet(BaseMatting):
+    """MODNet: Real-Time Trimap-Free Portrait Matting with ONNX Runtime.
+
+    MODNet produces a soft alpha matte from a full image without requiring
+    a trimap. It uses a MobileNetV2 backbone with low-resolution, high-resolution,
+    and fusion branches to generate accurate mattes at real-time speed.
+
+    Two pretrained variants are available:
+
+    - ``PHOTOGRAPHIC``: optimized for high-quality portrait photos.
+    - ``WEBCAM``: optimized for real-time webcam feeds.
+
+    Reference:
+        Ke et al., "MODNet: Real-Time Trimap-Free Portrait Matting via
+        Objective Decomposition", AAAI 2022.
+        https://github.com/yakhyo/modnet
+
+    Args:
+        model_name: The enum specifying the MODNet variant to load.
+            Defaults to ``MODNetWeights.PHOTOGRAPHIC``.
+        input_size: Target size for the shorter side during preprocessing.
+            The image is resized so its shorter side equals this value
+            (aspect ratio preserved), then both dimensions are floored to
+            multiples of 32. Defaults to 512.
+        providers: ONNX Runtime execution providers. If ``None``, auto-detects
+            the best available provider.
+
+    Attributes:
+        input_size (int): Target shorter-side size for preprocessing.
+
+    Example:
+        >>> from uniface.matting import MODNet
+        >>>
+        >>> matting = MODNet()
+        >>> matte = matting.predict(image)  # (H, W) float32 in [0, 1]
+        >>>
+        >>> # Composite onto green background
+        >>> import numpy as np
+        >>> bg = np.full_like(image, (0, 177, 64), dtype=np.uint8)
+        >>> alpha = matte[..., np.newaxis]
+        >>> result = (image * alpha + bg * (1 - alpha)).astype(np.uint8)
+    """
+
+    def __init__(
+        self,
+        model_name: MODNetWeights = MODNetWeights.PHOTOGRAPHIC,
+        input_size: int = 512,
+        providers: list[str] | None = None,
+    ) -> None:
+        Logger.info(f'Initializing MODNet with model={model_name}, input_size={input_size}')
+
+        self.input_size = input_size
+        self.providers = providers
+
+        self.model_path = verify_model_weights(model_name)
+        self._initialize_model()
+
+    def _initialize_model(self) -> None:
+        """Initialize the ONNX model from the stored model path.
+
+        Raises:
+            RuntimeError: If the model fails to load or initialize.
+        """
+        try:
+            self.session = create_onnx_session(self.model_path, providers=self.providers)
+
+            input_cfg = self.session.get_inputs()[0]
+            self.input_name = input_cfg.name
+
+            outputs = self.session.get_outputs()
+            self.output_names = [output.name for output in outputs]
+
+            Logger.info(f'MODNet initialized with input_size={self.input_size}')
+
+        except Exception as e:
+            Logger.error(f"Failed to load MODNet model from '{self.model_path}'", exc_info=True)
+            raise RuntimeError(f'Failed to initialize MODNet model: {e}') from e
+
+    def preprocess(self, image: np.ndarray) -> tuple[np.ndarray, int, int]:
+        """Preprocess a BGR image for MODNet inference.
+
+        The image is converted to RGB, resized so its shorter side matches
+        ``input_size`` (aspect ratio preserved), floored to multiples of 32,
+        and normalized to ``[-1, 1]``.
+
+        Args:
+            image: Input image in BGR format with shape ``(H, W, 3)``.
+
+        Returns:
+            A tuple of ``(tensor, orig_h, orig_w)`` where *tensor* has shape
+            ``(1, 3, H', W')`` in float32.
+        """
+        orig_h, orig_w = image.shape[:2]
+        rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+
+        if max(orig_h, orig_w) < self.input_size or min(orig_h, orig_w) > self.input_size:
+            if orig_w >= orig_h:
+                new_h = self.input_size
+                new_w = int(orig_w / orig_h * self.input_size)
+            else:
+                new_w = self.input_size
+                new_h = int(orig_h / orig_w * self.input_size)
+        else:
+            new_h, new_w = orig_h, orig_w
+
+        new_h = new_h - (new_h % STRIDE)
+        new_w = new_w - (new_w % STRIDE)
+        rgb = cv2.resize(rgb, (new_w, new_h), interpolation=cv2.INTER_AREA)
+
+        x = rgb.astype(np.float32) / 255.0
+        x = (x - 0.5) / 0.5
+        x = np.transpose(x, (2, 0, 1))
+
+        return np.expand_dims(x, axis=0), orig_h, orig_w
+
+    def postprocess(self, outputs: np.ndarray, original_size: tuple[int, int]) -> np.ndarray:
+        """Postprocess raw model output into an alpha matte.
+
+        Args:
+            outputs: Raw ONNX output with shape ``(1, 1, H', W')``.
+            original_size: Target size as ``(width, height)``.
+
+        Returns:
+            Alpha matte with shape ``(H, W)``, float32 in ``[0, 1]``.
+        """
+        matte = outputs[0, 0]
+        matte = cv2.resize(matte, original_size, interpolation=cv2.INTER_AREA)
+        return matte
+
+    def predict(self, image: np.ndarray) -> np.ndarray:
+        """Run portrait matting on a BGR image.
+
+        Args:
+            image: Input image in BGR format with shape ``(H, W, 3)``.
+
+        Returns:
+            Alpha matte with shape ``(H, W)``, float32 in ``[0, 1]``.
+        """
+        tensor, orig_h, orig_w = self.preprocess(image)
+        outputs = self.session.run(self.output_names, {self.input_name: tensor})
+        return self.postprocess(outputs[0], (orig_w, orig_h))

uniface/model_store.py

@@ -2,12 +2,6 @@
 # Author: Yakhyokhuja Valikhujaev
 # GitHub: https://github.com/yakhyo
 
-"""Model weight management for UniFace.
-
-This module handles downloading, caching, and verifying model weights
-using SHA-256 checksums for integrity validation.
-"""
-
 from __future__ import annotations
 
 from concurrent.futures import ThreadPoolExecutor, as_completed
@@ -159,7 +153,7 @@ def download_file(url: str, dest_path: str, timeout: int = 60, max_retries: int
                     unit_divisor=1024,
                 ) as progress,
             ):
-                for chunk in response.iter_content(chunk_size=const.CHUNK_SIZE):
+                for chunk in response.iter_content(chunk_size=const.DOWNLOAD_CHUNK_SIZE):
                     if chunk:
                         file.write(chunk)
                         progress.update(len(chunk))
@@ -178,7 +172,7 @@ def verify_file_hash(file_path: str, expected_hash: str) -> bool:
     """Compute the SHA-256 hash of the file and compare it with the expected hash."""
     file_hash = hashlib.sha256()
     with open(file_path, 'rb') as f:
-        for chunk in iter(lambda: f.read(const.CHUNK_SIZE), b''):
+        for chunk in iter(lambda: f.read(const.HASH_CHUNK_SIZE), b''):
             file_hash.update(chunk)
     actual_hash = file_hash.hexdigest()
     if actual_hash != expected_hash:
@@ -187,7 +181,7 @@ def verify_file_hash(file_path: str, expected_hash: str) -> bool:
 
 
 def download_models(
-    model_names: list[Enum], max_workers: int = 4, timeout: int = 60, max_retries: int = 3
+    model_names: list[Enum], max_workers: int | None = None, timeout: int = 60, max_retries: int = 3
 ) -> dict[Enum, str]:
     """Download and verify multiple models concurrently.
 
@@ -214,6 +208,17 @@ def download_models(
     results: dict[Enum, str] = {}
     errors: list[str] = []
 
+    if isinstance(max_workers, bool) or not isinstance(max_workers, int | None):
+        raise TypeError(f'max_workers must be int or None, got {type(max_workers).__name__}')
+
+    if max_workers is None or max_workers < 1:
+        if max_workers < 1:
+            Logger.info(f'max_workers must be >= 1, got {max_workers}; falling back to auto mode')
+        max_workers = min(os.cpu_count(), 8)  # at most 8
+
+    if max_workers < 1:
+        raise ValueError(f'max_workers must be >= 1, got {max_workers}')
+
     with ThreadPoolExecutor(max_workers=max_workers) as executor:
         future_to_model = {
             executor.submit(verify_model_weights, name, timeout=timeout, max_retries=max_retries): name

uniface/onnx_utils.py

@@ -12,7 +12,15 @@ from __future__ import annotations
 
 import functools
 
-import onnxruntime as ort
+try:
+    import onnxruntime as ort
+except ImportError as e:
+    raise ImportError(
+        'onnxruntime is not installed. Install it with one of:\n'
+        '  pip install uniface[cpu]   # CPU / Apple Silicon\n'
+        '  pip install uniface[gpu]   # NVIDIA GPU (CUDA)\n'
+        'Do not install both — they conflict.'
+    ) from e
 
 from uniface.log import Logger
 

uniface/privacy/blur.py

@@ -4,11 +4,14 @@
 
 from __future__ import annotations
 
-from typing import ClassVar
+from typing import TYPE_CHECKING, ClassVar
 
 import cv2
 import numpy as np
 
+if TYPE_CHECKING:
+    from uniface.types import Face
+
 __all__ = ['BlurFace', 'EllipticalBlur']
 
 
@@ -137,7 +140,7 @@ class BlurFace:
     def anonymize(
         self,
         image: np.ndarray,
-        faces: list,
+        faces: list[Face],
         inplace: bool = False,
     ) -> np.ndarray:
         """Anonymize faces in an image.

uniface/recognition/__init__.py

@@ -4,8 +4,11 @@
 
 
 from .adaface import AdaFace
+from .arcface import ArcFace
 from .base import BaseRecognizer
-from .models import ArcFace, MobileFace, SphereFace
+from .edgeface import EdgeFace
+from .mobileface import MobileFace
+from .sphereface import SphereFace
 
 
 def create_recognizer(method: str = 'arcface', **kwargs) -> BaseRecognizer:
@@ -18,7 +21,7 @@ def create_recognizer(method: str = 'arcface', **kwargs) -> BaseRecognizer:
 
     Args:
         method (str): The recognition method to use.
-            Options: 'arcface' (default), 'adaface', 'mobileface', 'sphereface'.
+            Options: 'arcface' (default), 'adaface', 'edgeface', 'mobileface', 'sphereface'.
         **kwargs: Model-specific parameters passed to the recognizer's constructor.
             For example, `model_name` can be used to select a specific
             pre-trained weight from the available enums (e.g., `ArcFaceWeights.MNET`).
@@ -43,6 +46,10 @@ def create_recognizer(method: str = 'arcface', **kwargs) -> BaseRecognizer:
 
         >>> # Create a SphereFace recognizer
         >>> recognizer = create_recognizer('sphereface')
+
+        >>> # Create an EdgeFace recognizer
+        >>> from uniface.constants import EdgeFaceWeights
+        >>> recognizer = create_recognizer('edgeface', model_name=EdgeFaceWeights.XXS)
     """
     method = method.lower()
 
@@ -50,13 +57,15 @@ def create_recognizer(method: str = 'arcface', **kwargs) -> BaseRecognizer:
         return ArcFace(**kwargs)
     elif method == 'adaface':
         return AdaFace(**kwargs)
+    elif method == 'edgeface':
+        return EdgeFace(**kwargs)
     elif method == 'mobileface':
         return MobileFace(**kwargs)
     elif method == 'sphereface':
         return SphereFace(**kwargs)
     else:
-        available = ['arcface', 'adaface', 'mobileface', 'sphereface']
+        available = ['arcface', 'adaface', 'edgeface', 'mobileface', 'sphereface']
         raise ValueError(f"Unsupported method: '{method}'. Available: {available}")
 
 
-__all__ = ['AdaFace', 'ArcFace', 'BaseRecognizer', 'MobileFace', 'SphereFace', 'create_recognizer']
+__all__ = ['AdaFace', 'ArcFace', 'BaseRecognizer', 'EdgeFace', 'MobileFace', 'SphereFace', 'create_recognizer']

uniface/recognition/arcface.py

@@ -0,0 +1,49 @@
+# Copyright 2025-2026 Yakhyokhuja Valikhujaev
+# Author: Yakhyokhuja Valikhujaev
+# GitHub: https://github.com/yakhyo
+
+from __future__ import annotations
+
+from uniface.constants import ArcFaceWeights
+from uniface.model_store import verify_model_weights
+
+from .base import BaseRecognizer, PreprocessConfig
+
+__all__ = ['ArcFace']
+
+
+class ArcFace(BaseRecognizer):
+    """ArcFace model for robust face recognition.
+
+    This class provides a concrete implementation of the BaseRecognizer,
+    pre-configured for ArcFace models. It handles the loading of specific
+    ArcFace weights and sets up the appropriate default preprocessing.
+
+    Args:
+        model_name (ArcFaceWeights): The specific ArcFace model variant to use.
+            Defaults to `ArcFaceWeights.MNET`.
+        preprocessing (Optional[PreprocessConfig]): An optional custom preprocessing
+            configuration. If None, a default config for ArcFace is used.
+        providers (list[str] | None): ONNX Runtime execution providers. If None, auto-detects
+            the best available provider. Example: ['CPUExecutionProvider'] to force CPU.
+
+    Example:
+        >>> from uniface.recognition import ArcFace
+        >>> recognizer = ArcFace()
+        >>> # embedding = recognizer.get_normalized_embedding(image, landmarks)
+
+    Reference:
+        https://arxiv.org/abs/1801.07698
+        https://github.com/yakhyo/face-reidentification
+    """
+
+    def __init__(
+        self,
+        model_name: ArcFaceWeights = ArcFaceWeights.MNET,
+        preprocessing: PreprocessConfig | None = None,
+        providers: list[str] | None = None,
+    ) -> None:
+        if preprocessing is None:
+            preprocessing = PreprocessConfig(input_mean=127.5, input_std=127.5, input_size=(112, 112))
+        model_path = verify_model_weights(model_name)
+        super().__init__(model_path=model_path, preprocessing=preprocessing, providers=providers)

uniface/recognition/edgeface.py

@@ -0,0 +1,57 @@
+# Copyright 2025-2026 Yakhyokhuja Valikhujaev
+# Author: Yakhyokhuja Valikhujaev
+# GitHub: https://github.com/yakhyo
+
+from __future__ import annotations
+
+from uniface.constants import EdgeFaceWeights
+from uniface.model_store import verify_model_weights
+
+from .base import BaseRecognizer, PreprocessConfig
+
+__all__ = ['EdgeFace']
+
+
+class EdgeFace(BaseRecognizer):
+    """EdgeFace: Efficient Face Recognition Model for Edge Devices.
+
+    EdgeFace uses an EdgeNeXt backbone with optional LoRA low-rank
+    compression, offering a strong accuracy-efficiency trade-off for
+    deployment on resource-constrained hardware. Competition-winning
+    entry (compact track) at EFaR 2023, IJCB.
+
+    All variants output 512-D embeddings from 112x112 aligned face crops.
+
+    Args:
+        model_name (EdgeFaceWeights): The specific EdgeFace model variant to use.
+            - XXS: Ultra-compact (1.24M params, ~5 MB)
+            - XS_GAMMA_06: Compact with LoRA (1.77M params, ~7 MB)
+            - S_GAMMA_05: Small with LoRA (3.65M params, ~14 MB)
+            - BASE: Full-size model (18.2M params, ~70 MB)
+            Defaults to `EdgeFaceWeights.XXS`.
+        preprocessing (Optional[PreprocessConfig]): An optional custom preprocessing
+            configuration. If None, a default config for EdgeFace is used.
+        providers (list[str] | None): ONNX Runtime execution providers. If None, auto-detects
+            the best available provider. Example: ['CPUExecutionProvider'] to force CPU.
+
+    Example:
+        >>> from uniface.recognition import EdgeFace
+        >>> recognizer = EdgeFace()
+        >>> # embedding = recognizer.get_normalized_embedding(image, landmarks)
+
+    Reference:
+        https://arxiv.org/abs/2307.01838v2
+        https://github.com/otroshi/edgeface
+        https://github.com/yakhyo/edgeface-onnx
+    """
+
+    def __init__(
+        self,
+        model_name: EdgeFaceWeights = EdgeFaceWeights.XXS,
+        preprocessing: PreprocessConfig | None = None,
+        providers: list[str] | None = None,
+    ) -> None:
+        if preprocessing is None:
+            preprocessing = PreprocessConfig(input_mean=127.5, input_std=127.5, input_size=(112, 112))
+        model_path = verify_model_weights(model_name)
+        super().__init__(model_path=model_path, preprocessing=preprocessing, providers=providers)

uniface/recognition/mobileface.py

@@ -0,0 +1,49 @@
+# Copyright 2025-2026 Yakhyokhuja Valikhujaev
+# Author: Yakhyokhuja Valikhujaev
+# GitHub: https://github.com/yakhyo
+
+from __future__ import annotations
+
+from uniface.constants import MobileFaceWeights
+from uniface.model_store import verify_model_weights
+
+from .base import BaseRecognizer, PreprocessConfig
+
+__all__ = ['MobileFace']
+
+
+class MobileFace(BaseRecognizer):
+    """Lightweight MobileFaceNet model for fast face recognition.
+
+    This class provides a concrete implementation of the BaseRecognizer,
+    pre-configured for MobileFaceNet models. It is optimized for speed,
+    making it suitable for edge devices.
+
+    Args:
+        model_name (MobileFaceWeights): The specific MobileFaceNet model variant to use.
+            Defaults to `MobileFaceWeights.MNET_V2`.
+        preprocessing (Optional[PreprocessConfig]): An optional custom preprocessing
+            configuration. If None, a default config for MobileFaceNet is used.
+        providers (list[str] | None): ONNX Runtime execution providers. If None, auto-detects
+            the best available provider. Example: ['CPUExecutionProvider'] to force CPU.
+
+    Example:
+        >>> from uniface.recognition import MobileFace
+        >>> recognizer = MobileFace()
+        >>> # embedding = recognizer.get_normalized_embedding(image, landmarks)
+
+    Reference:
+        https://arxiv.org/abs/1804.07573
+        https://github.com/yakhyo/face-recognition
+    """
+
+    def __init__(
+        self,
+        model_name: MobileFaceWeights = MobileFaceWeights.MNET_V2,
+        preprocessing: PreprocessConfig | None = None,
+        providers: list[str] | None = None,
+    ) -> None:
+        if preprocessing is None:
+            preprocessing = PreprocessConfig(input_mean=127.5, input_std=127.5, input_size=(112, 112))
+        model_path = verify_model_weights(model_name)
+        super().__init__(model_path=model_path, preprocessing=preprocessing, providers=providers)

uniface/recognition/models.py

@@ -1,112 +0,0 @@
-# Copyright 2025-2026 Yakhyokhuja Valikhujaev
-# Author: Yakhyokhuja Valikhujaev
-# GitHub: https://github.com/yakhyo
-
-from __future__ import annotations
-
-from uniface.constants import ArcFaceWeights, MobileFaceWeights, SphereFaceWeights
-from uniface.model_store import verify_model_weights
-
-from .base import BaseRecognizer, PreprocessConfig
-
-__all__ = ['ArcFace', 'MobileFace', 'SphereFace']
-
-
-class ArcFace(BaseRecognizer):
-    """ArcFace model for robust face recognition.
-
-    This class provides a concrete implementation of the BaseRecognizer,
-    pre-configured for ArcFace models. It handles the loading of specific
-    ArcFace weights and sets up the appropriate default preprocessing.
-
-    Args:
-        model_name (ArcFaceWeights): The specific ArcFace model variant to use.
-            Defaults to `ArcFaceWeights.MNET`.
-        preprocessing (Optional[PreprocessConfig]): An optional custom preprocessing
-            configuration. If None, a default config for ArcFace is used.
-        providers (list[str] | None): ONNX Runtime execution providers. If None, auto-detects
-            the best available provider. Example: ['CPUExecutionProvider'] to force CPU.
-
-    Example:
-        >>> from uniface.recognition import ArcFace
-        >>> recognizer = ArcFace()
-        >>> # embedding = recognizer.get_normalized_embedding(image, landmarks)
-    """
-
-    def __init__(
-        self,
-        model_name: ArcFaceWeights = ArcFaceWeights.MNET,
-        preprocessing: PreprocessConfig | None = None,
-        providers: list[str] | None = None,
-    ) -> None:
-        if preprocessing is None:
-            preprocessing = PreprocessConfig(input_mean=127.5, input_std=127.5, input_size=(112, 112))
-        model_path = verify_model_weights(model_name)
-        super().__init__(model_path=model_path, preprocessing=preprocessing, providers=providers)
-
-
-class MobileFace(BaseRecognizer):
-    """Lightweight MobileFaceNet model for fast face recognition.
-
-    This class provides a concrete implementation of the BaseRecognizer,
-    pre-configured for MobileFaceNet models. It is optimized for speed,
-    making it suitable for edge devices.
-
-    Args:
-        model_name (MobileFaceWeights): The specific MobileFaceNet model variant to use.
-            Defaults to `MobileFaceWeights.MNET_V2`.
-        preprocessing (Optional[PreprocessConfig]): An optional custom preprocessing
-            configuration. If None, a default config for MobileFaceNet is used.
-        providers (list[str] | None): ONNX Runtime execution providers. If None, auto-detects
-            the best available provider. Example: ['CPUExecutionProvider'] to force CPU.
-
-    Example:
-        >>> from uniface.recognition import MobileFace
-        >>> recognizer = MobileFace()
-        >>> # embedding = recognizer.get_normalized_embedding(image, landmarks)
-    """
-
-    def __init__(
-        self,
-        model_name: MobileFaceWeights = MobileFaceWeights.MNET_V2,
-        preprocessing: PreprocessConfig | None = None,
-        providers: list[str] | None = None,
-    ) -> None:
-        if preprocessing is None:
-            preprocessing = PreprocessConfig(input_mean=127.5, input_std=127.5, input_size=(112, 112))
-        model_path = verify_model_weights(model_name)
-        super().__init__(model_path=model_path, preprocessing=preprocessing, providers=providers)
-
-
-class SphereFace(BaseRecognizer):
-    """SphereFace model using angular margin for face recognition.
-
-    This class provides a concrete implementation of the BaseRecognizer,
-    pre-configured for SphereFace models, which were among the first to
-    introduce angular margin loss functions.
-
-    Args:
-        model_name (SphereFaceWeights): The specific SphereFace model variant to use.
-            Defaults to `SphereFaceWeights.SPHERE20`.
-        preprocessing (Optional[PreprocessConfig]): An optional custom preprocessing
-            configuration. If None, a default config for SphereFace is used.
-        providers (list[str] | None): ONNX Runtime execution providers. If None, auto-detects
-            the best available provider. Example: ['CPUExecutionProvider'] to force CPU.
-
-    Example:
-        >>> from uniface.recognition import SphereFace
-        >>> recognizer = SphereFace()
-        >>> # embedding = recognizer.get_normalized_embedding(image, landmarks)
-    """
-
-    def __init__(
-        self,
-        model_name: SphereFaceWeights = SphereFaceWeights.SPHERE20,
-        preprocessing: PreprocessConfig | None = None,
-        providers: list[str] | None = None,
-    ) -> None:
-        if preprocessing is None:
-            preprocessing = PreprocessConfig(input_mean=127.5, input_std=127.5, input_size=(112, 112))
-
-        model_path = verify_model_weights(model_name)
-        super().__init__(model_path=model_path, preprocessing=preprocessing, providers=providers)

uniface/recognition/sphereface.py

@@ -0,0 +1,50 @@
+# Copyright 2025-2026 Yakhyokhuja Valikhujaev
+# Author: Yakhyokhuja Valikhujaev
+# GitHub: https://github.com/yakhyo
+
+from __future__ import annotations
+
+from uniface.constants import SphereFaceWeights
+from uniface.model_store import verify_model_weights
+
+from .base import BaseRecognizer, PreprocessConfig
+
+__all__ = ['SphereFace']
+
+
+class SphereFace(BaseRecognizer):
+    """SphereFace model using angular margin for face recognition.
+
+    This class provides a concrete implementation of the BaseRecognizer,
+    pre-configured for SphereFace models, which were among the first to
+    introduce angular margin loss functions.
+
+    Args:
+        model_name (SphereFaceWeights): The specific SphereFace model variant to use.
+            Defaults to `SphereFaceWeights.SPHERE20`.
+        preprocessing (Optional[PreprocessConfig]): An optional custom preprocessing
+            configuration. If None, a default config for SphereFace is used.
+        providers (list[str] | None): ONNX Runtime execution providers. If None, auto-detects
+            the best available provider. Example: ['CPUExecutionProvider'] to force CPU.
+
+    Example:
+        >>> from uniface.recognition import SphereFace
+        >>> recognizer = SphereFace()
+        >>> # embedding = recognizer.get_normalized_embedding(image, landmarks)
+
+    Reference:
+        https://arxiv.org/abs/1704.08063
+        https://github.com/yakhyo/face-recognition
+    """
+
+    def __init__(
+        self,
+        model_name: SphereFaceWeights = SphereFaceWeights.SPHERE20,
+        preprocessing: PreprocessConfig | None = None,
+        providers: list[str] | None = None,
+    ) -> None:
+        if preprocessing is None:
+            preprocessing = PreprocessConfig(input_mean=127.5, input_std=127.5, input_size=(112, 112))
+
+        model_path = verify_model_weights(model_name)
+        super().__init__(model_path=model_path, preprocessing=preprocessing, providers=providers)

uniface/spoofing/minifasnet.py

@@ -6,6 +6,7 @@
 import cv2
 import numpy as np
 
+from uniface.common import softmax
 from uniface.constants import MiniFASNetWeights
 from uniface.log import Logger
 from uniface.model_store import verify_model_weights
@@ -179,11 +180,6 @@ class MiniFASNet(BaseSpoofer):
 
         return face
 
-    def _softmax(self, x: np.ndarray) -> np.ndarray:
-        """Apply softmax to logits along axis 1."""
-        e_x = np.exp(x - np.max(x, axis=1, keepdims=True))
-        return e_x / e_x.sum(axis=1, keepdims=True)
-
     def postprocess(self, outputs: np.ndarray) -> SpoofingResult:
         """
         Postprocess raw model outputs into prediction result.
@@ -197,7 +193,7 @@ class MiniFASNet(BaseSpoofer):
         Returns:
             SpoofingResult: Result containing is_real flag and confidence score.
         """
-        probs = self._softmax(outputs)
+        probs = softmax(outputs)
         label_idx = int(np.argmax(probs))
         confidence = float(probs[0, label_idx])