One API for every face task under the sun

What it does

UniFace is a Python integration library that bundles roughly a dozen distinct face-analysis tasks behind one API. It handles detection, alignment, recognition, landmark extraction, gaze and head-pose estimation, segmentation, matting, attribute classification, anti-spoofing, quality scoring, and anonymization. Models download automatically on first use and are verified by SHA-256, so the library is usable out of the box without hunting for weights.

The interesting bit

The real value is architectural, not algorithmic: the author has already resolved the tedious wiring between disparate model families—RetinaFace, SCRFD, ArcFace, MediaPipe, MODNet, BiSeNet, MiniFASNet, and others—so you can swap detectors or recognizers without rewriting your pipeline. A FaceAnalyzer class even provides zero-config defaults if you just want results.

Key highlights

• Broad model coverage: detection (RetinaFace, YOLO-family, BlazeFace), recognition (ArcFace, AdaFace, SphereFace), and dense landmarks up to 478-point MediaPipe meshes.
• Production-oriented extras: FAISS-backed vector search, BYTETracker for persistent face IDs across video frames, and five built-in anonymization blur modes.
• Hardware flexibility: runs on CPU, NVIDIA CUDA, and ARM64/Apple Silicon via ONNXRuntime.
• Optional predictors are modular; you load only what you need, from age-gender estimation to trimap-free portrait matting.

Verdict

Use this if you are building a prototype or pipeline that needs multiple face modalities and you would rather maintain one dependency tree than a dozen. Skip it if you need a single, heavily optimized model and are willing to tune it yourself.