ArcFace Face Recognition Access Control System

A high-accuracy face recognition system using ArcFace for access control applications with an emphasis on security, reliability, and ease of use.

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🌟 Key Features

• State-of-the-art facial recognition using ArcFace model (99.82% accuracy on LFW benchmark)
• Real-time processing optimized for access control applications
• Modular architecture with separate components for detection, embedding, and verification
• Comprehensive database management tools for adding, removing, and updating identities
• Multiple database options including standard storage and FAISS for high-scale deployments
• Detailed logging and access records for security auditing
• Extensive configuration options to balance security and convenience
• Simple command-line interface for all operations

📋 Table of Contents

• System Requirements
• Installation
• Quick Start
• Configuration Options
• Database Management
• Technical Architecture
• Recognition Performance
• Troubleshooting
• Contributing
• License
• Acknowledgments

💻 System Requirements

Minimum Requirements

• Python 3.7+
• 4GB RAM
• Intel/AMD x86-64 CPU (2 cores+)
• 500MB disk space
• Webcam (for live recognition)

Recommended Requirements

• Python 3.8+
• 8GB RAM
• Modern Intel/AMD CPU (4+ cores)
• NVIDIA GPU with CUDA support
• 1GB+ disk space
• HD webcam or IP camera

Tested Platforms

• Windows 10/11
• Ubuntu 20.04/22.04
• macOS 11+

🚀 Installation

From GitHub

# Clone the repository
git clone https://github.com/yourusername/face-recognition-system.git
cd face-recognition-system

# Create and activate virtual environment (optional but recommended)
python -m venv venv
source venv/bin/activate  # Linux/Mac
# or
venv\Scripts\activate  # Windows

# Install dependencies
pip install -r requirements.txt

With GPU Support (Optional)

# Install GPU versions of libraries for better performance
pip install onnxruntime-gpu faiss-gpu

🏁 Quick Start

1. Create Face Database

First, organize your face images into folders named after each person:

face_data/
├── John_Smith/
│   ├── image1.jpg
│   ├── image2.jpg
│   └── ...
├── Jane_Doe/
│   ├── image1.jpg
│   └── ...
└── ...

Then, create the database:

python main.py create-db --root face_data

2. Test Recognition

Test with a single image:

python main.py test --image path/to/test.jpg

3. Run Live Recognition

python main.py live

Press 'q' to quit, 's' to take a screenshot.

⚙️ Configuration Options

Configuration settings are located in config/settings.py. Key parameters:

Parameter	Default	Description
DETECTION_THRESHOLD	0.6	Face detection confidence threshold
RECOGNITION_THRESHOLD	0.5	Similarity threshold for recognition
USE_FAISS	False	Whether to use FAISS for database
MODEL_NAME	buffalo_l	InsightFace model name
DET_SIZE	(640, 640)	Detection size
CTX_ID	0	CUDA device ID (0 for first GPU, -1 for CPU)

Using Command-Line Options

You can override configuration options directly from the command line:

# Adjust recognition threshold for stricter matching
python main.py live --recognition-threshold 0.65

# Use FAISS for larger databases
python main.py live --use-faiss

# Use CPU for detection (when no GPU available)
python main.py live --ctx-id -1

📊 Database Management

The system includes comprehensive tools for managing face databases:

# List all identities
python tools/manage_database.py list

# Add a new person from multiple images
python tools/manage_database.py add --name "John Smith" --folder path/to/john_photos

# Remove a person
python tools/manage_database.py remove --name "John Smith"

# Update an existing person's face data
python tools/manage_database.py update --name "Jane Doe" --folder path/to/new_photos

# Backup the database
python tools/manage_database.py backup

Best Practices for Database Management

• Use 5-10 high-quality images per person from different angles
• Update embeddings when a person's appearance changes significantly
• Create regular backups, especially before major updates
• Test recognition accuracy periodically with known samples
• For large databases (500+ identities), switch to FAISS

🏗️ Technical Architecture

The system follows a modular design with clear separation of concerns:

face_recognition_system/
├── config/               # Configuration settings
├── src/                  # Source code modules
│   ├── database/         # Database operations
│   ├── face/             # Face detection and embedding
│   ├── utils/            # Utility functions
│   └── access_control/   # Access verification
├── tools/                # Command-line tools
└── main.py               # Main entry point

Recognition Pipeline

1. Face Detection: Using RetinaFace from InsightFace
2. Face Alignment: Based on detected facial landmarks
3. Embedding Generation: Using ArcFace (ResNet-50) generating 512D embeddings
4. Embedding Comparison: Using cosine similarity for matching
5. Access Decision: Based on similarity threshold

Database Storage Options

1. Standard Database: Pickle-based storage for small to medium deployments

   • Fast for databases with up to ~500 identities
   • Simple backup and restore

2. FAISS Database: For large-scale deployments

   • Optimized for databases with 500+ identities
   • Significant performance advantage for large databases
   • More complex backup and restore processes

🎯 Recognition Performance

Model Details

The system uses ArcFace with the following specifications:

• Model: InsightFace's buffalo_l (ResNet-50 backbone)
• Embedding Size: 512-dimensional vector
• Comparison Method: Cosine similarity with normalization
• LFW Benchmark: 99.82% accuracy

Tuning Recognition Parameters

Threshold	False Accepts	False Rejects	Typical Use Case
0.3-0.4	Higher	Lower	Convenience-focused
0.5	Balanced	Balanced	General usage
0.6-0.7	Lower	Higher	Security-focused

For high-security applications, increase the recognition threshold to 0.65 or higher.

Performance Benchmarks

Database Size	Standard DB	FAISS DB
10 identities	2ms	5ms
100 identities	15ms	7ms
1000 identities	130ms	10ms

Measured on Intel i7 CPU with 16GB RAM. GPU acceleration improves times by 40-60%.

❓ Troubleshooting

Common Issues

OpenCV GUI Error

Error: The function is not implemented. Rebuild the library with Windows, GTK+ 2.x or Cocoa support.

Solution: Install OpenCV with GUI support:

pip uninstall opencv-python
pip install opencv-contrib-python

Face Not Detected

If faces aren't being detected properly:

1. Check lighting conditions - ensure even, bright lighting
2. Adjust DETECTION_THRESHOLD in settings (lower for better detection)
3. Verify camera is working correctly
4. Test with different DET_SIZE values

Poor Recognition Accuracy

If recognition results are unreliable:

1. Add more varied training images for the person
2. Ensure face is clearly visible in the training images
3. Check for database quality issues (use tools/manage_database.py test)
4. Adjust RECOGNITION_THRESHOLD based on your needs

Getting Help

If you encounter issues:

1. Check the logs in the logs/ directory
2. Open an issue on GitHub with detailed information
3. Include log files and system information

👥 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• InsightFace for the ArcFace implementation
• OpenCV for computer vision utilities
• FAISS for efficient similarity search
• All contributors who have helped improve this system

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Developed with ❤️ for advanced, ethical face recognition