deepface and openface
DeepFace is a higher-level, actively maintained Python library for practical face recognition and attribute analysis, while OpenFace is a lower-level deep learning framework for training custom face recognition models—making them complements that could be used together in a pipeline where OpenFace generates embeddings that DeepFace consumes.
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 openface
cmusatyalab/openface
Face recognition with deep neural networks.
Implements face embedding via triplet loss on deep convolutional networks to generate compact 128-D representations enabling efficient face comparison and clustering. Includes batch processing pipelines, real-time webcam classification, and evaluation scripts against standard LFW benchmarks, with training utilities for custom models built on Torch and integrated with dlib's landmark detection.
Related comparisons
Scores updated daily from GitHub, PyPI, and npm data. How scores work