# DeepFace — Lightweight Face Recognition and Attribute Analysis Framework

> DeepFace wraps multiple face recognition backends including VGG-Face, FaceNet, ArcFace, and Dlib behind a simple Python API for face verification, identification, and demographic attribute prediction.

## Install

Save in your project root:

# DeepFace — Lightweight Face Recognition and Attribute Analysis Framework

## Quick Use
```bash
pip install deepface
python -c "
from deepface import DeepFace
result = DeepFace.verify(img1_path='img1.jpg', img2_path='img2.jpg')
print(result['verified'], result['distance'])
analysis = DeepFace.analyze(img_path='img1.jpg', actions=['age', 'gender', 'emotion'])
print(analysis)
"
```

## Introduction
DeepFace is a Python framework that unifies multiple face recognition models behind a single API. It handles face detection, alignment, representation, verification, and demographic analysis, letting developers swap backends without rewriting application code.

## What DeepFace Does
- Verifies whether two face images belong to the same person
- Searches a face database to find matching identities
- Predicts age, gender, emotion, and ethnicity from face images
- Detects and aligns faces using OpenCV, MTCNN, RetinaFace, or MediaPipe
- Generates face embeddings using selectable backends like ArcFace, Facenet, or VGG-Face

## Architecture Overview
DeepFace loads a recognition model and a detector on first use, then caches weights for subsequent calls. The verify function extracts embeddings from both images and computes cosine or Euclidean distance against a threshold. The analyze function runs lightweight attribute classifiers on aligned face crops. All models are Keras or ONNX-based and download automatically.

## Self-Hosting & Configuration
- Install with pip; TensorFlow or ONNX Runtime is pulled as a dependency
- Set the model_name parameter to switch between ArcFace, Facenet512, VGG-Face, or others
- Choose a detector backend via detector_backend for speed-accuracy trade-offs
- Use DeepFace.stream() for real-time webcam verification
- Deploy as a REST API with the built-in Flask server via deepface serve

## Key Features
- Supports 10 recognition models and 7 face detectors in a single interface
- Real-time streaming verification from webcam feeds
- Built-in REST API server for microservice deployment
- Automatic model download and weight caching
- Anti-spoofing module to reject printed or screen-displayed faces

## Comparison with Similar Tools
- **InsightFace** — provides the trained models directly; DeepFace wraps external models for convenience
- **face_recognition (dlib)** — single backend; DeepFace offers many selectable models
- **MediaPipe Face** — focuses on real-time landmarks; DeepFace targets recognition and attributes
- **CompreFace** — REST-first face recognition service; DeepFace is a Python library with an optional API
- **Amazon Rekognition** — managed cloud service; DeepFace runs locally with no API fees

## FAQ
**Q: Which recognition model should I choose?**
A: ArcFace (default) and Facenet512 offer the best accuracy on most benchmarks. VGG-Face works well for smaller datasets.

**Q: Can DeepFace run without a GPU?**
A: Yes. CPU inference is fully supported, though batch processing of large galleries is faster on a GPU.

**Q: How do I build a face database for identification?**
A: Organize images into folders named per identity, then call DeepFace.find(img_path, db_path). It builds an in-memory index on first run.

**Q: Is DeepFace suitable for production use?**
A: The library is widely used in prototyping and internal tools. For high-throughput production, consider wrapping it behind a queue or using the built-in REST server.

## Sources
- https://github.com/serengil/deepface
- https://pypi.org/project/deepface/

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Author: AI Open Source