• Real-Time Face Recognition: Live processing from webcam or video files with optimized performance
• Production-Ready Accuracy: State-of-the-art SCRFD + ArcFace models for reliable detection and recognition
• Intelligent Processing: Smart batch optimization that adapts to face count for optimal performance
• Scalable Vector Search: FAISS-powered similarity search with thread-safe database operations
• Flexible Model Selection: Multiple model sizes available to balance speed vs accuracy for your use case
• Robust Resource Management: Built-in memory leak prevention and automatic cleanup for long-running applications

• Intelligent Batch Processing: Automatically switches between sequential and parallel processing based on face count
• Thread-Safe Database: Robust FAISS integration with proper resource management
• Memory Efficient: Context managers ensure proper cleanup and prevent memory leaks
• Optimized for Video: Designed for typical video scenarios (1-5 faces per frame)

The system combines three powerful components:

1. SCRFD (Paper): Efficient face detection
2. ArcFace (Paper): Robust face recognition
3. FAISS: Fast similarity search for face re-identification

├── assets/
│   ├── demo.mp4
│   |── in_video.mp4
|   └── faces/           # Place target face images here
│     ├── face1.jpg
│     ├── face2.jpg
│     └── ...
├── database/           # FAISS database implementation
├── models/            # Neural network models
├── weights/           # Model weights (download required)
├── utils/            # Helper functions
├── main.py           # Main application entry
└── requirements.txt  # Dependencies

1. Clone the repository:

git clone https://github.com/yakyo/face-reidentification.git
cd face-reidentification

2. Install dependencies:

pip install -r requirements.txt

3. Download model weights:

Quick download (Linux/Mac):

sh download.sh

4. Add target faces: Place face images in assets/faces/ directory. The filename will be used as the person's identity.

python main.py --source assets/in_video.mp4

usage: main.py [-h] [--det-weight DET_WEIGHT] [--rec-weight REC_WEIGHT] 
               [--similarity-thresh SIMILARITY_THRESH] [--confidence-thresh CONFIDENCE_THRESH]
               [--faces-dir FACES_DIR] [--source SOURCE] [--max-num MAX_NUM]

• Face database automatically saves/loads from disk
• Intelligent processing: sequential for <10 faces, parallel for larger batches
• Thread-safe operations with proper resource cleanup

• Typical video processing: Sequential processing is automatically used for 1-5 faces per frame
• Threading optimization: Parallel processing kicks in only for 10+ faces (rare in normal video)
• GPU acceleration: Install onnxruntime-gpu for faster model inference
• Memory efficiency: Context managers ensure proper resource cleanup in long-running applications
• Database persistence: Face database automatically saves/loads, avoiding rebuild on restart

1. SCRFD: Sample and Computation Redistribution for Efficient Face Detection
2. ArcFace: Additive Angular Margin Loss for Deep Face Recognition