serengil/deepface

A Lightweight Face Recognition and Facial Attribute Analysis (Age, Gender, Emotion and Race) Library for Python

What it solves

DeepFace provides a unified, lightweight Python framework for face recognition and facial attribute analysis. It simplifies the complex pipeline of face detection, alignment, normalization, and representation, allowing developers to perform identity verification and attribute extraction without needing deep expertise in the underlying computer vision processes.

How it works

DeepFace acts as a hybrid wrapper for several state-of-the-art face recognition models (such as VGG-Face, FaceNet, ArcFace, and GhostFaceNet). It automates a five-stage pipeline: detecting the face, aligning it, normalizing it, representing it as a multi-dimensional vector (embedding), and verifying the identity based on distance metrics like Cosine Similarity or Euclidean Distance. It also includes specialized modules for analyzing facial attributes and detecting spoofing attempts.

Who it’s for

It is designed for Python developers who need to integrate face recognition, identity verification, or facial demographic analysis into their applications without building the infrastructure from scratch.

Highlights

  • Multi-Model Support: Wraps a wide array of models including VGG-Face, FaceNet, OpenFace, DeepID, ArcFace, and more.
  • Facial Attribute Analysis: Predicts age, gender, emotion (e.g., happy, sad, angry), and race.
  • Flexible Search: Supports both directory-based face datastores and database-backed searches with support for vector databases like pgvector, Pinecone, Milvus, and Qdrant.
  • Diverse Detection Backends: Integrates multiple detectors including RetinaFace, MediaPipe, YOLO, and MTCNN.
  • Anti-Spoofing: Includes a module to determine if a facial image is real or a fake/spoof.
  • Real-Time Capabilities: Provides a stream function for real-time webcam analysis.

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