UniFace v1.0.0 arrives as an MIT-licensed, open source toolkit built by Yakhyo Valikhujaev to simplify and accelerate face biometrics development for production use.
UniFace v1.0.0 has been released as a stable open source library designed to consolidate face detection, facial recognition, facial landmarking and attribute analysis into a single, production-ready stack. Launched under an MIT licence and actively maintained on GitHub, the library positions itself as an accessible, high-performance alternative to fragmented and proprietary face-biometrics ecosystems.
Developed by Yakhyo Valikhujaev, UniFace is presented as “an all-in-one face analysis toolkit designed for real-world use.” It addresses long-standing developer challenges such as library juggling, complex dependency chains and performance optimisation hurdles that typically slow down the creation of robust face analysis pipelines.
UniFace leverages ONNX Runtime for automatic hardware acceleration and supports Apple silicon, Nvidia GPUs and CPU environments, enabling broad deployment across research and production systems. The library includes two face-detection model families, ArcFace and MobileFace embedding models, 106-point facial landmark detection, and models covering age estimation, gender classification and emotion recognition.
The toolkit is suitable for face search and real-time webcam-based detection, with detailed guidance available for implementing age and gender recognition as well as batch processing features. Valikhujaev emphasises its developer-first design philosophy, saying: “Lightweight, easy to integrate, and optimized for performance — UniFace is ready for your research, prototypes, or production pipelines.”
