Responsible Face Authentication System

A privacy-focused facial authentication system implementing responsible AI principles for secure and ethical biometric verification.

Overview

This system provides face-based authentication while prioritizing:

• Privacy and data protection
• Transparency and explainability
• Performance monitoring
• Informed consent
• Ethical considerations

Project Structure

responsible_face_authentication/
│
├── src/
│   ├── __init__.py
│   ├── core/
│   │   ├── __init__.py
│   │   ├── image_processor.py
│   │   ├── face_comparator.py
│   │   └── constants.py
│   ├── security/
│   │   ├── __init__.py
│   │   ├── privacy_manager.py
│   │   └── consent_manager.py
│   ├── monitoring/
│   │   ├── __init__.py
│   │   ├── model_monitor.py
│   │   └── performance_tracker.py
│   └── utils/
│       ├── __init__.py
│       └── helpers.py
├── data/
│   ├── secure_storage/
│   └── monitoring/
├── docs/
│   └── model_card.json
├── tests/
│   ├── __init__.py
│   ├── test_face_comparator.py
│   └── test_privacy_manager.py
├── config/
│   └── settings.py
├── main.py
├── requirements.txt
└── README.md

Key Features

Privacy & Security

• Data encryption using

PrivacyManager

• Automated data deletion after retention period
• Explicit consent management
• Secure storage of biometric data
• Encryption of images and sensitive data

Responsible AI Implementation

• Model performance monitoring via

ModelMonitor

• Drift detection and calibration checks
• Detailed explanation of verification decisions
• Quality checks for facial images
• Bias monitoring across demographics

Core Functionality

• Face detection and verification using DeepFace
• Image quality assessment via

ImagePreprocessor

• Multiple face detection backends
• Configurable confidence thresholds

Technical Stack

# Core Dependencies
deepface         # Face analysis
opencv-python    # Image processing
mediapipe       # Face detection
numpy           # Numerical operations
cryptography    # Data encryption
PyYAML          # Configuration management

Responsible AI in Computer Vision

Responsible AI ensures AI systems are:

1. Ethical: Respecting privacy and human rights
2. Transparent: Explaining decisions and processes
3. Accountable: Monitoring and measuring performance
4. Fair: Avoiding biases and discrimination

Implementation Details

1. Privacy Protection

• Encrypted storage of biometric data
• Automatic deletion after retention period
• Explicit consent management
• Minimal data collection

2. Transparency

• Detailed explanations of verification decisions
• Model cards documenting system behavior
• Clear documentation of data usage

3. Performance Monitoring

• Continuous drift detection
• Quality metrics tracking
• Performance across demographics
• Regular calibration checks

4. Security Measures

• Fernet encryption for data
• Secure key management
• Access controls
• Audit logging

Configuration

System settings in

settings.py

:

{
    "face_detection": {
        "model": "Facenet",
        "detector": "RetinaFace",
        "min_confidence": [default]
    },
    "privacy": {
        "retention_days": 30,
        "storage_path": "./data/secure_storage",
        "encryption_enabled": True
    }
}

Usage

from src.core.face_comparator import compare_photos_with_explanations
from src.security.privacy_manager import PrivacyManager
from src.monitoring.model_monitor import ModelMonitor

# Initialize components
privacy_manager = PrivacyManager('./data/secure_storage')
model_monitor = ModelMonitor('facial_comparison_v1')

# Compare faces with explanations
result = compare_photos_with_explanations(
    reference_image_path,
    live_image_path,
    user_id,
    privacy_manager,
    model_monitor
)

Contributing

Please read our Contribution Guidelines and code of ethics before submitting pull requests.

Developer

Muhit Khan

• LinkedIn: linkedin.com/in/muhit-khan
• Email: muhit.dev@gmail.com

License

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