Healthcare Face Recognition System

A secure, privacy-preserving face recognition system specifically designed for healthcare environments. The system automates patient identification, provides optional AI-based genetic disorder screening, implements comprehensive bias detection, and includes strong anti-spoofing security measures.

🏥 Features

Core Functionality

• Privacy-First Patient Identification: Secure facial recognition with encryption and consent management
• Genetic Disorder Pre-Screening: AI-based facial feature analysis for preliminary genetic disorder detection
• Bias Detection & Correction: Comprehensive fairness monitoring across demographic groups
• Anti-Spoofing Security: Multi-method liveness detection to prevent fraudulent use
• HIPAA/GDPR Compliance: Built-in privacy controls and audit logging

Security Features

• AES-256 encryption for all sensitive data
• JWT-based authentication and authorization
• Role-based access controls
• Comprehensive audit logging
• Anti-spoofing with multiple liveness detection methods
• Secure key management

Privacy Features

• Patient consent management system
• Data retention policies
• Anonymized audit logs
• On-device processing capabilities
• Encrypted facial embeddings
• Right to be forgotten implementation

🚀 Quick Start

Prerequisites

• Python 3.8+
• OpenCV
• TensorFlow 2.13+
• SQLite
• Redis (optional, for caching)

Installation

1. Clone the repository

   git clone <repository-url>
   cd face_recognition

2. Create virtual environment

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies

   pip install -r requirements.txt

4. Configure environment

   cp .env.example .env
   # Edit .env with your configuration

5. Initialize database

   python -c "from src.core.patient_identification import PatientIdentificationSystem; PatientIdentificationSystem()"

6. Run the application

   python main.py

The API will be available at http://localhost:8000

📖 API Documentation

Core Endpoints

Patient Registration

POST /register-patient
Content-Type: multipart/form-data

{
  "patient_id": "P001",
  "name": "John Doe",
  "date_of_birth": "1990-01-01",
  "medical_record_number": "MRN123456",
  "consent_given": true,
  "image": <image_file>
}

Patient Identification

POST /identify-patient
Content-Type: multipart/form-data

{
  "session_id": "session_123",
  "image": <image_file>
}

Consent Management

POST /update-consent
Content-Type: application/json

{
  "patient_id": "P001",
  "consent_type": "face_recognition",
  "consent_given": true
}

Genetic Screening

GET /genetic-screening/{patient_id}
Authorization: Bearer <jwt_token>

Fairness Reports

GET /fairness-report
Authorization: Bearer <jwt_token>

Response Examples

Successful Identification

{
  "status": "identified",
  "patient_id": "P001",
  "name": "John Doe",
  "confidence_score": 0.95,
  "processing_time_ms": 150.5,
  "genetic_screening_results": [
    {
      "disorder": "down_syndrome",
      "risk_score": 0.3,
      "confidence": 0.8,
      "recommendation": "Low risk for Down Syndrome. No immediate genetic testing recommended.",
      "disclaimer": "This is a preliminary screening tool..."
    }
  ]
}

🏗️ Architecture

System Components

┌─────────────────────────────────────────────────────────────┐
│                    Healthcare FR System                     │
├─────────────────────────────────────────────────────────────┤
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐         │
│  │   Patient   │  │  Genetic    │  │    Bias     │         │
│  │Identification│  │ Screening   │  │ Monitoring  │         │
│  └─────────────┘  └─────────────┘  └─────────────┘         │
├─────────────────────────────────────────────────────────────┤
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐         │
│  │   Privacy   │  │  Security   │  │   Consent   │         │
│  │  Manager    │  │  Manager    │  │  Manager    │         │
│  └─────────────┘  └─────────────┘  └─────────────┘         │
├─────────────────────────────────────────────────────────────┤
│                    Encrypted Database                       │
└─────────────────────────────────────────────────────────────┘

Data Flow

1. Patient Registration

   • Face image captured
   • Facial embedding extracted and encrypted
   • Patient data encrypted and stored
   • Consent recorded

2. Patient Identification

   • Face image captured
   • Anti-spoofing checks performed
   • Facial embedding extracted
   • Encrypted database searched
   • Consent verified
   • Optional genetic screening performed

3. Bias Monitoring

   • Performance metrics collected by demographic group
   • Fairness metrics calculated
   • Bias alerts generated
   • Recommendations provided

🔒 Security & Privacy

Encryption

• AES-256-CBC encryption for all sensitive data
• PBKDF2-HMAC-SHA256 key derivation
• HMAC-SHA256 authentication
• Encrypted facial embeddings
• Secure key management

Anti-Spoofing

• Blink Detection: Analyzes eye movement patterns
• Head Movement: Detects natural head motion
• Texture Analysis: Identifies real vs. fake textures
• Multi-frame Analysis: Temporal consistency checks
• Liveness Scoring: Combined confidence assessment

Privacy Compliance

• HIPAA Compliance: Healthcare data protection
• GDPR Compliance: EU privacy regulations
• Consent Management: Granular consent controls
• Data Retention: Configurable retention policies
• Audit Logging: Comprehensive access tracking
• Right to be Forgotten: Data deletion capabilities

🧬 Genetic Screening

Supported Disorders

• Down Syndrome
• Williams Syndrome
• Angelman Syndrome
• Prader-Willi Syndrome
• Noonan Syndrome
• Turner Syndrome
• Marfan Syndrome
• Achondroplasia

Features

• Facial Feature Analysis: 68-point landmark detection
• Risk Scoring: AI-based risk assessment
• Confidence Metrics: Reliability indicators
• Clinical Recommendations: Actionable guidance
• Medical Disclaimers: Clear limitations

⚖️ Bias Detection

Fairness Metrics

• Demographic Parity: Equal positive prediction rates
• Equalized Odds: Equal TPR and FPR across groups
• Equal Opportunity: Equal TPR across groups
• Disparate Impact: 80% rule compliance
• Statistical Parity: Equal prediction distributions

Monitoring

• Real-time Bias Detection: Continuous monitoring
• Demographic Group Analysis: Performance by group
• Fairness Reports: Comprehensive assessments
• Recommendations: Bias mitigation guidance
• Alert System: Threshold-based notifications

🛠️ Configuration

Environment Variables

Key configuration options in .env:

# Security
SECURITY_ENCRYPTION_KEY=your_encryption_key
SECURITY_JWT_SECRET=your_jwt_secret

# Privacy
PRIVACY_DATA_RETENTION_DAYS=365
PRIVACY_ENABLE_CONSENT_MANAGEMENT=true

# Models
MODEL_CONFIDENCE_THRESHOLD=0.85
MODEL_GENETIC_SCREENING_THRESHOLD=0.7

# Bias Detection
BIAS_BIAS_THRESHOLD=0.1
BIAS_ENABLE_BIAS_MONITORING=true

Database Configuration

The system uses SQLite by default but can be configured for other databases:

DATABASE_DATABASE_URL=sqlite:///./data/encrypted_db/healthcare_fr.db
# or
DATABASE_DATABASE_URL=postgresql://user:pass@localhost/healthcare_fr

📊 Monitoring & Analytics

System Statistics

• Patient identification rates
• Consent compliance metrics
• Bias detection statistics
• Genetic screening results
• Security incident tracking

Performance Metrics

• Processing times
• Accuracy rates by demographic group
• Model performance tracking
• Resource utilization

Audit Logs

• All patient identifications
• Consent changes
• System access
• Data modifications
• Security events

🧪 Testing

Run Tests

pytest tests/ -v

Test Coverage

pytest tests/ --cov=src --cov-report=html

Security Testing

# Run security tests
pytest tests/security/ -v

# Run bias detection tests
pytest tests/bias_detection/ -v

🚀 Deployment

Docker Deployment

# Build image
docker build -t healthcare-fr .

# Run container
docker run -p 8000:8000 healthcare-fr

Production Considerations

• Use HTTPS in production
• Configure proper JWT secrets
• Set up database backups
• Enable monitoring and alerting
• Configure log aggregation
• Set up security scanning

📝 License

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

🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests
5. Submit a pull request

📞 Support

For support and questions:

• Create an issue in the repository
• Contact the development team
• Check the documentation

⚠️ Disclaimer

This system is designed for assistive screening purposes only. It is NOT a diagnostic tool and should NOT replace professional medical evaluation. Any positive results should be confirmed through proper genetic testing and consultation with qualified healthcare professionals.

🔄 Version History

• v1.0.0: Initial release with core functionality
  • Patient identification
  • Genetic screening
  • Bias detection
  • Privacy controls
  • Security features