uniface 2.2.0

UniFace: A Comprehensive Library for Face Detection, Recognition, Landmark Analysis, Face Parsing, Gaze Estimation, Age, and Gender Detection

UniFace: All-in-One Face Analysis Library

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.

💬 Have questions? Chat with this codebase on DeepWiki - AI-powered docs that let you ask anything about UniFace.

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Features

• Face Detection — RetinaFace, SCRFD, YOLOv5-Face, and YOLOv8-Face with 5-point landmarks
• Face Recognition — ArcFace, MobileFace, and SphereFace embeddings
• Facial Landmarks — 106-point landmark localization
• Face Parsing — BiSeNet semantic segmentation (19 classes)
• Gaze Estimation — Real-time gaze direction with MobileGaze
• Attribute Analysis — Age, gender, race (FairFace), and emotion
• Anti-Spoofing — Face liveness detection with MiniFASNet
• Face Anonymization — 5 blur methods for privacy protection
• Hardware Acceleration — ARM64 (Apple Silicon), CUDA (NVIDIA), CPU

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Installation

# Standard installation
pip install uniface

# GPU support (CUDA)
pip install uniface[gpu]

# From source
git clone https://github.com/yakhyo/uniface.git
cd uniface && pip install -e .

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Quick Example

import cv2
from uniface import RetinaFace

# Initialize detector (models auto-download on first use)
detector = RetinaFace()

# Detect faces
image = cv2.imread("photo.jpg")
faces = detector.detect(image)

for face in faces:
    print(f"Confidence: {face.confidence:.2f}")
    print(f"BBox: {face.bbox}")
    print(f"Landmarks: {face.landmarks.shape}")

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Documentation

📚 Full documentation: yakhyo.github.io/uniface

Resource	Description
Quickstart	Get up and running in 5 minutes
Model Zoo	All models, benchmarks, and selection guide
API Reference	Detailed module documentation
Tutorials	Step-by-step workflow examples
Guides	Architecture and design principles

Jupyter Notebooks

Example	Colab	Description
01_face_detection.ipynb		Face detection and landmarks
02_face_alignment.ipynb		Face alignment for recognition
03_face_verification.ipynb		Compare faces for identity
04_face_search.ipynb		Find a person in group photos
05_face_analyzer.ipynb		All-in-one analysis
06_face_parsing.ipynb		Semantic face segmentation
07_face_anonymization.ipynb		Privacy-preserving blur
08_gaze_estimation.ipynb		Gaze direction estimation

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References

• yakhyo/retinaface-pytorch — RetinaFace training
• yakhyo/yolov5-face-onnx-inference — YOLOv5-Face ONNX
• yakhyo/yolov8-face-onnx-inference — YOLOv8-Face ONNX
• yakhyo/face-recognition — ArcFace, MobileFace, SphereFace
• yakhyo/face-parsing — BiSeNet face parsing
• yakhyo/gaze-estimation — MobileGaze training
• yakhyo/face-anti-spoofing — MiniFASNet inference
• yakhyo/fairface-onnx — FairFace attributes
• deepinsight/insightface — Model architectures

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Contributing

Contributions are welcome! Please see CONTRIBUTING.md for guidelines.

License

This project is licensed under the MIT License.