uniface 3.0.0

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

UniFace: All-in-One Face Analysis Library

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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.

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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
• Face Tracking — Multi-object tracking with BYTETracker for persistent IDs across video frames
• Facial Landmarks — 106-point landmark localization module (separate from 5-point detector landmarks)
• Face Parsing — BiSeNet semantic segmentation (19 classes), XSeg face masking
• 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 (latest version)

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

Optional dependencies

• Emotion model uses TorchScript and requires torch: pip install torch (choose the correct build for your OS/CUDA)
• YOLOv5-Face and YOLOv8-Face support faster NMS with torchvision: pip install torch torchvision then use nms_mode='torchvision'

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Model Downloads and Cache

Models are downloaded automatically on first use and verified via SHA-256.

Default cache location: ~/.uniface/models

Override with the programmatic API or environment variable:

from uniface.model_store import get_cache_dir, set_cache_dir

set_cache_dir('/data/models')
print(get_cache_dir())  # /data/models

export UNIFACE_CACHE_DIR=/data/models

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Quick Example (Detection)

import cv2
from uniface.detection import RetinaFace

detector = RetinaFace()

image = cv2.imread("photo.jpg")
if image is None:
    raise ValueError("Failed to load image. Check the path to '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}")

Face Detection Model Output

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Example (Face Analyzer)

import cv2
from uniface.analyzer import FaceAnalyzer
from uniface.detection import RetinaFace
from uniface.recognition import ArcFace

detector = RetinaFace()
recognizer = ArcFace()

analyzer = FaceAnalyzer(detector, recognizer=recognizer)

image = cv2.imread("photo.jpg")
if image is None:
    raise ValueError("Failed to load image. Check the path to 'photo.jpg'.")

faces = analyzer.analyze(image)

for face in faces:
    print(face.bbox, face.embedding.shape if face.embedding is not None else None)

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Execution Providers (ONNX Runtime)

from uniface.detection import RetinaFace

# Force CPU-only inference
detector = RetinaFace(providers=["CPUExecutionProvider"])

See more in the docs: https://yakhyo.github.io/uniface/concepts/execution-providers/

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Documentation

Full documentation: https://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

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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
09_face_segmentation.ipynb		Face segmentation with XSeg

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Licensing and Model Usage

UniFace is MIT-licensed, but several pretrained models carry their own licenses. Review: https://yakhyo.github.io/uniface/license-attribution/

Notable examples:

• YOLOv5-Face and YOLOv8-Face weights are GPL-3.0
• FairFace weights are CC BY 4.0

If you plan commercial use, verify model license compatibility.

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References

Feature	Repository	Training	Description
Detection	retinaface-pytorch	✓	RetinaFace PyTorch Training & Export
Detection	yolov5-face-onnx-inference	-	YOLOv5-Face ONNX Inference
Detection	yolov8-face-onnx-inference	-	YOLOv8-Face ONNX Inference
Tracking	bytetrack-tracker	-	BYTETracker Multi-Object Tracking
Recognition	face-recognition	✓	MobileFace, SphereFace Training
Parsing	face-parsing	✓	BiSeNet Face Parsing
Parsing	face-segmentation	-	XSeg Face Segmentation
Gaze	gaze-estimation	✓	MobileGaze Training
Anti-Spoofing	face-anti-spoofing	-	MiniFASNet Inference
Attributes	fairface-onnx	-	FairFace ONNX Inference

*SCRFD and ArcFace models are from InsightFace.

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Contributing

Contributions are welcome. Please see CONTRIBUTING.md.

Support

If you find this project useful, consider giving it a ⭐ on GitHub — it helps others discover it!

Questions or feedback:

• Discord: https://discord.gg/wdzrjr7R5j
• GitHub Issues: https://github.com/yakhyo/uniface/issues
• DeepWiki Q&A: https://deepwiki.com/yakhyo/uniface

License

This project is licensed under the MIT License.