Real-Time Face Re-Identification with FAISS, ArcFace & SCRFD

Tip

The models and functionality in this repository are integrated into UniFace — an all-in-one face analysis library.

output.mp4

Key Features

• Real-Time Face Recognition: Process webcam or video files with SCRFD detection and ArcFace embeddings
• FAISS Similarity Search: Batch cosine-similarity lookup using a FAISS inner-product index
• Multiple Model Sizes: Choose from lightweight to high-accuracy detection and recognition models
• Minimal Dependencies: Built on ONNX Runtime, OpenCV, NumPy, and FAISS with no extra frameworks

Note

Place your target face images in the assets/faces/ directory. The filenames will be used as identity labels during recognition.

Components

1. SCRFD — Sample and Computation Redistribution for Efficient Face Detection
2. ArcFace — Additive Angular Margin Loss for Deep Face Recognition
3. FAISS — Facebook AI Similarity Search

Available Models

Category	Model	Size	Description
Detection	SCRFD 500M	2.41 MB	Lightweight face detection
Detection	SCRFD 2.5G	3.14 MB	Balanced performance
Detection	SCRFD 10G	16.1 MB	High accuracy
Recognition	ArcFace MobileFace	12.99 MB	Mobile-friendly recognition
Recognition	ArcFace ResNet-50	166 MB	High-accuracy recognition

Project Structure

├── 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

Getting Started

Prerequisites

Important

Make sure you have Python 3.10+ installed on your system.

Installation

1. Clone the repository:

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

2. Install dependencies:

pip install -r requirements.txt

3. Download model weights:

Click to see download links 📥

Model	Download Link	Size
SCRFD 500M	det_500m.onnx	2.41 MB
SCRFD 2.5G	det_2.5g.onnx	3.14 MB
SCRFD 10G	det_10g.onnx	16.1 MB
ArcFace MobileFace	w600k_mbf.onnx	12.99 MB
ArcFace ResNet-50	w600k_r50.onnx	166 MB

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.

Usage

Basic Usage

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

Command Line Arguments

Tip

Use these arguments to customize the recognition behavior:

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]

Argument	Description	Default
--det-weight	Detection model path	./weights/det_10g.onnx
--rec-weight	Recognition model path	./weights/w600k_mbf.onnx
--similarity-thresh	Face similarity threshold	0.4
--confidence-thresh	Detection confidence threshold	0.5
--faces-dir	Target faces directory	./assets/faces
--source	Video source (file or camera index)	./assets/in_video.mp4
--max-num	Max faces per frame (0 = unlimited)	0
--db-path	Custom database storage location	./database/face_database
--update-db	Force rebuild face database	False
--output	Specify output video path	output_video.mp4

Technical Notes

• Face database is saved to and loaded from disk automatically; no rebuild needed on restart
• All detected faces in a frame are queried in a single FAISS index.search() call
• For GPU-accelerated inference, install onnxruntime-gpu instead of onnxruntime

References

Note

This project builds upon the following research:

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