uniface 3.5.2

UniFace: A Unified Face Analysis Library for Python

UniFace: A Unified Face Analysis Library for Python

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UniFace is a lightweight, production-ready Python library for face detection, recognition, tracking, landmark analysis, face parsing, gaze estimation, and face attributes.

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Features

• Face Detection — RetinaFace, SCRFD, YOLOv5-Face, and YOLOv8-Face with 5-point landmarks
• Face Recognition — AdaFace, ArcFace, EdgeFace, 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
• Portrait Matting — Trimap-free alpha matte with MODNet (background removal, green screen, compositing)
• Gaze Estimation — Real-time gaze direction with MobileGaze
• Head Pose Estimation — 3D head orientation (pitch, yaw, roll) with 6D rotation representation
• Attribute Analysis — Age, gender, race (FairFace), and emotion
• Vector Store — FAISS-backed embedding store for fast multi-identity search
• Anti-Spoofing — Face liveness detection with MiniFASNet
• Face Anonymization — 5 blur methods for privacy protection
• Hardware Acceleration — ARM64 (Apple Silicon), CUDA (NVIDIA), CPU

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Visual Examples

Face Detection	Gaze Estimation
Head Pose Estimation	Age & Gender
Face Verification
106-Point Landmarks
Face Parsing
Face Segmentation
Portrait Matting
Face Anonymization

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Installation

CPU / Apple Silicon

pip install uniface[cpu]

GPU support (NVIDIA CUDA)

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)

git clone https://github.com/yakhyo/uniface.git
cd uniface && pip install -e ".[cpu]"   # or .[gpu] for CUDA

FAISS vector store

pip install faiss-cpu   # or faiss-gpu for CUDA

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 import FaceAnalyzer

# Zero-config: uses SCRFD (500M) + ArcFace (MobileNet) by default
analyzer = FaceAnalyzer()

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)

With attributes:

from uniface import FaceAnalyzer, AgeGender

analyzer = FaceAnalyzer(attributes=[AgeGender()])
faces = analyzer.analyze(image)

for face in faces:
    print(f"{face.sex}, {face.age}y, embedding={face.embedding.shape}")

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Example (Portrait Matting)

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)

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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		Unified face 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
10_face_vector_store.ipynb		FAISS-backed face database
11_head_pose_estimation.ipynb		Head pose estimation (pitch, yaw, roll)
12_face_recognition.ipynb		Standalone face recognition pipeline
13_portrait_matting.ipynb		Portrait matting with MODNet

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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
Datasets	Training data and evaluation benchmarks

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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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Datasets

Task	Training Dataset	Models
Detection	WIDER FACE	RetinaFace, SCRFD, YOLOv5-Face, YOLOv8-Face
Recognition	MS1MV2	MobileFace, SphereFace
Recognition	WebFace600K	ArcFace
Recognition	WebFace4M / 12M	AdaFace
Recognition	MS1MV2	EdgeFace
Gaze	Gaze360	MobileGaze
Head Pose	300W-LP	HeadPose (ResNet, MobileNet)
Parsing	CelebAMask-HQ	BiSeNet
Attributes	CelebA, FairFace, AffectNet	AgeGender, FairFace, Emotion

See Datasets documentation for download links, benchmarks, and details.

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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
Recognition	edgeface-onnx	-	EdgeFace ONNX Inference
Parsing	face-parsing	✓	BiSeNet Face Parsing
Parsing	face-segmentation	-	XSeg Face Segmentation
Gaze	gaze-estimation	✓	MobileGaze Training
Head Pose	head-pose-estimation	✓	Head Pose Training (6DRepNet-style)
Matting	modnet	-	MODNet Portrait Matting
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.

Disclaimer: This project is not affiliated with or related to Uniface by Rocket Software.