The Secure Biometric Analysis System is designed to provide enterprise-grade biometric processing with a focus on security, performance, and accuracy. By combining Python's rich computer vision ecosystem with Rust's systems programming capabilities, we deliver a robust solution for biometric analysis and secure template management.

mindmap
  root((Secure Biometric<br/>System))
    Biometric Processing
      Video Capture
        Frame Optimization
        Quality Assessment
      Facial Analysis
        Landmark Detection
        Feature Extraction
      3D Processing
        Depth Mapping
        Mesh Generation
    Security
      Template Encryption
      Access Control
      Audit Logging
      Compliance
    Performance
      Parallel Processing
      GPU Acceleration
      Caching
      Load Balancing
    Integration
      REST APIs
      gRPC Services
      WebSocket Support

Our system follows a dual-language architecture that maximizes the strengths of both Python and Rust:

graph TD
    subgraph "Python Frontend"
        A[Video Input] --> B[Frame Processing]
        B --> C[Feature Extraction]
        C --> D[3D Analysis]
        D --> E[Template Generation]
    end

    subgraph "Rust Backend"
        F[Template Storage] --> G[Encryption Layer]
        G --> H[Secure Database]
        I[Template Matching] --> J[Parallel Processor]
        K[API Gateway] --> L[Auth Service]
    end

    E -->|Secure Channel| K
    L -->|Templates| F
    L -->|Match Request| I
    H -->|Encrypted Data| I

    style A fill:#93c5fd,stroke:#1d4ed8
    style E fill:#93c5fd,stroke:#1d4ed8
    style K fill:#fca5a5,stroke:#b91c1c
    style H fill:#fca5a5,stroke:#b91c1c

sequenceDiagram
    participant C as Client
    participant P as Python Engine
    participant R as Rust Backend
    participant D as Database

    C->>P: Video Stream
    activate P
    P->>P: Frame Processing
    P->>P: Feature Extraction
    P->>P: 3D Analysis
    P->>R: Template
    deactivate P
    
    activate R
    R->>R: Encrypt Template
    R->>D: Store Template
    R->>R: Process Match
    R->>C: Result
    deactivate R

Our testing infrastructure ensures reliability, security, and performance across all components:

graph TD
    A[Testing Framework] --> B[Security Tests]
    A --> C[Performance Tests]
    A --> D[Integration Tests]
    A --> E[Storage Tests]
    
    B --> B1[Encryption]
    B --> B2[Key Management]
    B --> B3[Data Integrity]
    
    C --> C1[Load Testing]
    C --> C2[Resource Usage]
    C --> C3[Benchmarks]
    
    D --> D1[API Testing]
    D --> D2[End-to-End]
    
    E --> E1[Template Storage]
    E --> E2[Database Ops]

The Rust backend features comprehensive test coverage across critical components:

• Security Testing

  • Encryption/decryption operations
  • Key rotation mechanisms
  • Nonce uniqueness verification
  • Tampering detection
  • Data integrity validation

• Performance Testing

  • Template retrieval: ~215μs per template
  • Database writes: 3,500+ ops/sec
  • Batch storage: ~300μs per template
  • Memory usage monitoring
  • Concurrent operation handling

• Integration Testing

  • API endpoint validation
  • Error handling scenarios
  • Component interaction verification
  • End-to-end flow testing

For detailed testing documentation, see Rust Testing Documentation.

• Real-time video capture and optimization
• 68-point facial landmark detection
• Advanced feature analysis
• 3D depth mapping and mesh generation
• Expression analysis
• Visualization tools

• Encrypted template storage
• High-performance template matching
• Parallel processing capabilities
• Compliance management
• Audit logging
• Access control

graph TD
    A[Input Data] --> B[Encryption Layer]
    B --> C[Secure Storage]
    
    D[Access Request] --> E[Authentication]
    E --> F[Authorization]
    F --> G[Audit Logging]
    
    H[Template Match] --> I[Secure Channel]
    I --> J[Result]
    
    style B fill:#fca5a5,stroke:#b91c1c
    style E fill:#fca5a5,stroke:#b91c1c
    style I fill:#fca5a5,stroke:#b91c1c

• AES-256 encryption for templates
• Secure key management
• Access control and authentication
• Comprehensive audit logging
• GDPR and CCPA compliance

• Parallel processing with Rayon
• GPU acceleration for computations
• Memory-optimized data structures
• Strategic caching
• Load balancing for high availability

• Python Process Documentation
• Rust Backend Design
• Project State
• Changelog

opencv-python>=4.8.0
dlib>=19.24.0
numpy>=1.24.0
pytorch>=2.0.0
open3d>=0.17.0

[dependencies]
tokio = { version = "1.35", features = ["full"] }
actix-web = "4.4"
sled = "0.34"
ring = "0.17"
uuid = { version = "1.6", features = ["v4", "serde"] }
serde = { version = "1.0", features = ["derive"] }
serde_json = "1.0"
thiserror = "1.0"
rayon = "1.7"
prometheus = "0.13"

• ChaCha20-Poly1305 encryption for improved performance
• Enhanced key rotation with proper concurrency handling
• Secure template storage with encryption at rest
• Comprehensive security test suite

• Template retrieval: ~215μs per template
• Database writes: 3,500+ ops/sec
• Batch storage: ~300μs per template
• High-throughput database configuration
• Optimized concurrent operations

• Comprehensive test framework
• Security-focused test suite
• Performance benchmarking
• Integration testing
• Detailed test documentation

For a complete list of changes, see our Changelog.

Coming Soon

The project is currently in active development. Setup and usage instructions will be provided as components are implemented.

We welcome contributions! Please read our contributing guidelines (coming soon) before submitting pull requests.

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

• OpenCV community
• dlib developers
• Rust community
• Security researchers