MATLAB Implementation of KL Transform for Face Recognition

This documentation presents a face recognition implementation using KL (Karhunen-Loève) transform in MATLAB. The technique performs face identification through image transformation and comparison processes. The KL transform serves as an effective dimensionality reduction method that converts facial images into characteristic feature vectors, enabling deeper analysis and processing of image data. The implementation includes covariance matrix calculation for image datasets, eigenvalue decomposition to extract principal components, and projection of facial images onto the feature subspace. Key MATLAB functions employed involve cov() for covariance computation, eig() for eigenvalue decomposition, and matrix operations for feature vector projection. This comprehensive package contains detailed procedural explanations, step-by-step implementation guidelines, and all necessary supporting files and materials. By studying this documentation, you will gain practical understanding of applying KL transform for face recognition technology and can immediately begin related research and development activities. The code structure follows modular design principles, separating data preprocessing, transformation matrix calculation, and recognition comparison into distinct functional units for maintainability and extensibility.