Super-Resolution Enhanced Face Recognition

This project addresses the critical challenge of face recognition in surveillance systems where captured images are often of poor quality due to distance, lighting conditions, or camera limitations. By implementing a Super-Resolution Generative Adversarial Network (SR-GAN), we significantly improve recognition accuracy on low-quality face images.

Problem Statement

Surveillance cameras frequently capture face images that are:

Low Resolution: Limited pixel information due to distance or camera constraints
Poor Quality: Affected by lighting, motion blur, or compression artifacts
Challenging for Recognition: Standard face recognition systems struggle with degraded inputs

Technical Approach

Super-Resolution GAN Architecture

Generator Network: Transforms low-resolution inputs to high-resolution outputs
Discriminator Network: Ensures realistic and high-quality super-resolved images
Adversarial Training: Generator and discriminator compete to improve image quality
Perceptual Loss: Maintains facial features and identity-relevant information

Integration with Face Recognition

Two-Stage Pipeline: Super-resolution followed by face recognition
End-to-End Optimization: Joint training for optimal recognition performance
Feature Preservation: Maintains discriminative facial characteristics during upsampling

Performance Achievements

Quantitative Results

37% Improvement: Recognition rate increase on down-sampled test images
5% Overall Gain: Performance improvement across the entire proprietary dataset
Robust Performance: Consistent improvements across various quality degradations

Technical Metrics

PSNR Enhancement: Significant peak signal-to-noise ratio improvements
SSIM Scores: Better structural similarity to high-quality references
Recognition Accuracy: Substantial gains in face identification tasks

Dataset & Evaluation

Proprietary Surveillance Dataset: Real-world low-quality face images
Diverse Conditions: Various lighting, angles, and quality degradations
Comprehensive Testing: Multiple resolution levels and quality metrics
Baseline Comparisons: Performance against standard recognition systems

Applications & Impact

This technology has significant implications for:

Security Systems: Enhanced surveillance and access control
Law Enforcement: Improved suspect identification from CCTV footage
Border Control: Better passport and ID verification systems
Commercial Applications: Retail analytics and customer recognition

Technical Innovation

Domain-Specific Training: Tailored for surveillance image characteristics
Quality-Aware Processing: Adaptive enhancement based on input degradation
Real-Time Capability: Optimized for practical deployment scenarios
Scalable Architecture: Efficient processing for large-scale surveillance networks

The project demonstrates how generative adversarial networks can be effectively applied to enhance face recognition systems, particularly in challenging real-world surveillance scenarios where image quality is often compromised.

GitHub Repository: anushrisuresh/LRFR

Technologies: GANs, Super-Resolution, Face Recognition, Computer Vision, Deep Learning, PyTorch