A comprehensive facial recognition system demonstrating advanced technical implementation and ethical considerations in AI-based recognition technologies. This project features a custom-built facial recognition pipeline with detection, matching, anonymization, and bias testing capabilities.
This advanced facial recognition system combines computer vision techniques with ethical considerations to create a robust, versatile application for face detection, identity matching, privacy protection, and algorithmic bias analysis. The system allows users to:
- Detect and locate faces in images and video feeds
- Match detected faces against known reference images
- Apply privacy-preserving filters to detected faces
- Analyze demographic bias in recognition performance
- Manage datasets for testing and analysis
Beyond technical functionality, the project serves as a practical exploration of important ethical questions surrounding facial recognition technologies, including privacy, consent, and algorithmic fairness.
- Modular design with clear separation of concerns
- Backend/frontend separation for improved maintainability
- Plugin-based architecture allowing feature extensions
- Factory pattern implementation for component creation
- Object-oriented design with inheritance hierarchies
- Strategic use of design patterns (Factory, Singleton, Observer)
- Type hinting and signature standardization
- Custom exception hierarchy for domain-specific errors
- Graceful degradation for hardware failures (camera/display)
- Detailed error reporting with context preservation
- Defensive programming with input validation
- Multi-stage build for optimized image size
- Cross-platform compatibility (Linux, macOS, Windows)
- Volume mapping for development workflow
- Health checks and container lifecycle management
- Environment configuration via Docker Compose
- Automated testing on push/pull request
- Environment matrix testing (Python versions, OS)
- Dependency verification and security scanning
- Build artifact generation
- Hierarchical configuration with inheritance
- Environment variable overrides for deployment flexibility
- Runtime configuration updates
- Pytest implementation with fixtures and parameterization
- Unit tests for core functionality validation
- Integration tests for component interactions
- Functional tests for end-to-end workflows
- Mocking for hardware dependencies
- Multi-level logging (DEBUG, INFO, WARNING, ERROR, CRITICAL)
- Rotating file handlers with size limits
- Contextual logging with module and function information
- Log message standardization and formatting
- Integration of OpenCV and face_recognition libraries
- Custom face detection and encoding pipeline
- Multiple privacy-preserving techniques (blur, pixelate, mask)
- Performance optimization for real-time processing
- Interactive frontend for all recognition features
- Responsive layout with custom CSS styling
- Component-based architecture for reusability
- Custom visualization components for bias analysis
- Static image processing with multiple detection algorithms
- Adjustable confidence thresholds for detection sensitivity
- Visualization with bounding boxes and coordinates
- Comparison of detected faces against reference database
- Configurable matching thresholds for identification
- Confidence scoring for match quality
- Reference face management system
- Multiple privacy protection methods:
- Gaussian blur with adjustable intensity
- Pixelation with configurable resolution reduction
- Masking with face icon overlay
- Real-time anonymization processing
- Anonymized output file generation
- Demographic performance analysis across ethnic groups
- Statistical bias quantification (variance, standard deviation)
- Visualization of recognition accuracy disparities
- UTKFace dataset integration with demographic annotations
- UTKFace dataset download and extraction
- Demographic split preparation for bias testing
- Reference face extraction and organization
- Test dataset creation and management
The project implements a layered architecture with clear separation between:
- Core Recognition Engine: Backend implementation of face detection, matching, and anonymization
- Utility Layer: Common functionality, configuration, and logging
- Interface Layer: User interfaces including Streamlit web application
Data flows through well-defined interfaces between layers, with each component having a single responsibility and clear dependency management.
The system implements a comprehensive error handling strategy with:
- Custom exception hierarchy for domain-specific errors
- Contextual error information preservation
- Graceful degradation for hardware failures
- User-friendly error messages with troubleshooting guidance
Every operation is tracked with:
- Entry and exit logging for key methods
- Performance metrics logging for critical operations
- ERROR level logging for exceptional conditions
- Multi-destination logging (console, files)
# Clone the repository
git clone https://github.com/vignankamarthi/Facial-Recognition-Software-Project.git
cd Facial-Recognition-Software-Project
# Start with Docker Compose
cd docker
docker-compose up -d
# Access the application at http://localhost:8501git clone https://github.com/vignankamarthi/Facial-Recognition-Software-Project.git
cd Facial-Recognition-Software-Project
pip install -r requirements.txt
python run_demo.py| Feature | Access Method | Description |
|---|---|---|
| Face Detection | Select "Face Detection" in navigation | Detect faces in uploaded images or webcam feed |
| Face Matching | Select "Face Matching" in navigation | Match faces against reference database |
| Face Anonymization | Select "Face Anonymization" in navigation | Apply privacy filters to detected faces |
| Bias Testing | Select "Bias Testing" in navigation | Analyze recognition performance across demographics |
| Dataset Management | Select "Dataset Management" in navigation | Download and prepare datasets |
The project implements demographic bias testing using the UTKFace dataset, which provides:
- Quantitative measurement of accuracy differences across ethnic groups
- Statistical analysis of algorithmic bias
- Visualization of performance disparities
- Educational framework for understanding fairness challenges
Multiple privacy-preserving techniques are provided:
- Face anonymization methods (blur, pixelate, mask)
- Reference face management with consent principles
- Data handling recommendations
The UTKFace dataset integration enables:
- Testing across ethnic categories (White, Black, Asian, Indian, Others)
- Age and gender demographic analysis
- Statistical significance evaluation of performance differences
- Intersectional analysis of demographic factors
Facial-Recognition-Software-Project/
├── config/ # Configuration files
├── data/ # Data directory
├── docker/ # Docker configuration
├── docs/ # Documentation
├── logs/ # System logs
├── src/ # Source code
│ ├── backend/ # Core recognition functionality
│ ├── utils/ # Utility modules
│ └── ui/ # User interfaces
├── tests/ # Test suite
├── .github/workflows/ # GitHub CI/CD configuration
├── run_demo.py # Demo launcher
└── requirements.txt # Dependencies
For detailed documentation about the project structure and implementation details, see PROJECT_STRUCTURE.md.
For ethical discussions about facial recognition technology, see docs/ethical_discussion.md.
For troubleshooting help, see docs/troubleshooting.md.