uniface/docs/modules/recognition.md

Recognition

Face recognition extracts embeddings for identity verification and face search.

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Available Models

Model	Backbone	Size	Embedding Dim	Best For
ArcFace	MobileNet/ResNet	8-166 MB	512	General use (recommended)
MobileFace	MobileNet V2/V3	1-10 MB	512	Mobile/Edge
SphereFace	Sphere20/36	50-92 MB	512	Research

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ArcFace

State-of-the-art recognition using additive angular margin loss.

Basic Usage

from uniface import RetinaFace, ArcFace

detector = RetinaFace()
recognizer = ArcFace()

# 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}")  # (1, 512)

Model Variants

from uniface import ArcFace
from uniface.constants import ArcFaceWeights

# Lightweight (default)
recognizer = ArcFace(model_name=ArcFaceWeights.MNET)

# High accuracy
recognizer = ArcFace(model_name=ArcFaceWeights.RESNET)

Variant	Backbone	Size	Use Case
MNET :material-check-circle:	MobileNet	8 MB	Balanced (recommended)
RESNET	ResNet50	166 MB	Maximum accuracy

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MobileFace

Lightweight recognition for resource-constrained environments.

Basic Usage

from uniface import MobileFace

recognizer = MobileFace()
embedding = recognizer.get_normalized_embedding(image, landmarks)

Model Variants

from uniface import MobileFace
from uniface.constants import MobileFaceWeights

# Ultra-lightweight
recognizer = MobileFace(model_name=MobileFaceWeights.MNET_025)

# Balanced (default)
recognizer = MobileFace(model_name=MobileFaceWeights.MNET_V2)

# Higher accuracy
recognizer = MobileFace(model_name=MobileFaceWeights.MNET_V3_LARGE)

Variant	Params	Size	LFW	Use Case
MNET_025	0.36M	1 MB	98.8%	Ultra-lightweight
MNET_V2 :material-check-circle:	2.29M	4 MB	99.6%	Mobile/Edge
MNET_V3_SMALL	1.25M	3 MB	99.3%	Mobile optimized
MNET_V3_LARGE	3.52M	10 MB	99.5%	Balanced mobile

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SphereFace

Recognition using angular softmax loss (A-Softmax).

Basic Usage

from uniface import SphereFace
from uniface.constants import SphereFaceWeights

recognizer = SphereFace(model_name=SphereFaceWeights.SPHERE20)
embedding = recognizer.get_normalized_embedding(image, landmarks)

Variant	Params	Size	LFW	Use Case
SPHERE20	24.5M	50 MB	99.7%	Research
SPHERE36	34.6M	92 MB	99.7%	Research

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Face Comparison

Compute Similarity

from uniface import compute_similarity
import numpy as np

# Extract embeddings
emb1 = recognizer.get_normalized_embedding(image1, landmarks1)
emb2 = recognizer.get_normalized_embedding(image2, landmarks2)

# Method 1: Using utility function
similarity = compute_similarity(emb1, emb2)

# Method 2: Direct computation
similarity = np.dot(emb1, emb2.T)[0][0]

print(f"Similarity: {similarity:.4f}")

Threshold Guidelines

Threshold	Decision	Use Case
> 0.7	Very high confidence	Security-critical
> 0.6	Same person	General verification
0.4 - 0.6	Uncertain	Manual review needed
< 0.4	Different people	Rejection

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Face Alignment

Recognition models require aligned faces. UniFace handles this internally:

# Alignment is done automatically
embedding = recognizer.get_normalized_embedding(image, landmarks)

# Or manually align
from uniface import face_alignment

aligned_face = face_alignment(image, landmarks)
# Returns: 112x112 aligned face image

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Building a Face Database

import numpy as np
from uniface import RetinaFace, ArcFace

detector = RetinaFace()
recognizer = ArcFace()

# Build database
database = {}
for person_id, image_path in person_images.items():
    image = cv2.imread(image_path)
    faces = detector.detect(image)

    if faces:
        embedding = recognizer.get_normalized_embedding(image, faces[0].landmarks)
        database[person_id] = embedding

# Save for later use
np.savez('face_database.npz', **database)

# Load database
data = np.load('face_database.npz')
database = {key: data[key] for key in data.files}

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Face Search

Find a person in a database:

def search_face(query_embedding, database, threshold=0.6):
    """Find best match in database."""
    best_match = None
    best_similarity = -1

    for person_id, db_embedding in database.items():
        similarity = np.dot(query_embedding, db_embedding.T)[0][0]

        if similarity > best_similarity and similarity > threshold:
            best_similarity = similarity
            best_match = person_id

    return best_match, best_similarity

# Usage
query_embedding = recognizer.get_normalized_embedding(query_image, landmarks)
match, similarity = search_face(query_embedding, database)

if match:
    print(f"Found: {match} (similarity: {similarity:.4f})")
else:
    print("No match found")

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Factory Function

from uniface import create_recognizer

recognizer = create_recognizer('arcface')

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See Also

• Detection Module - Detect faces first
• Face Search Recipe - Complete search system
• Thresholds - Calibration guide