MATLAB Implementation of K-L Transform for Face Recognition

This is an exceptional MATLAB implementation of K-L transform-based face recognition, featuring a robust template matching library that ensures high-efficiency performance in facial identification tasks. The source code demonstrates outstanding performance metrics and recognition accuracy, making it suitable for widespread applications in biometric security systems and computer vision projects. The implementation utilizes advanced statistical pattern recognition algorithms, specifically the Karhunen-Loève expansion for optimal feature extraction from facial images. Key algorithmic components include covariance matrix computation, eigenvalue decomposition for dimensionality reduction, and principal component analysis to create efficient facial templates. The code architecture offers flexibility and extensibility, allowing developers to customize threshold parameters, modify template databases, and integrate additional preprocessing modules according to specific project requirements. Core functions include image normalization, eigenface generation, similarity measurement using Euclidean distance or cosine similarity, and adaptive matching thresholds. This reliable and powerful face recognition solution supports various image formats and adapts to multiple deployment scenarios including real-time identification systems and batch processing applications.