deepface and faced
DeepFace provides comprehensive face recognition and attribute analysis across multiple dimensions, while Faced offers only lightweight CPU-based face detection as a preprocessing step, making them complements that could be used in sequence rather than alternatives.
About deepface
serengil/deepface
A Lightweight Face Recognition and Facial Attribute Analysis (Age, Gender, Emotion and Race) Library for Python
Built on a modular pipeline architecture, DeepFace wraps multiple state-of-the-art face recognition models (VGG-Face, FaceNet, ArcFace, Dlib, etc.) to handle detection, alignment, normalization, representation, and verification in a unified API. Beyond pairwise verification, it supports large-scale face recognition through both directory-based and database-backed search with approximate nearest neighbor indexing across PostgreSQL, MongoDB, Neo4j, Pinecone, and Weaviate backends. The library achieves >97% accuracy on facial recognition benchmarks while abstracting away the underlying deep learning complexity.
About faced
iitzco/faced
🚀 😏 Near Real Time CPU Face detection using deep learning
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