Face Recognition Using KL Transformation and Principal Component Analysis

This mini-program implements face recognition using KL Transform (Karhunen-Loève Transform) and Principal Component Analysis (PCA) through MATLAB. The application processes and analyzes facial images to extract distinctive facial features, which are then compared against a known face database for automated facial recognition and identity verification. By employing algorithms like KL Transform and PCA, the system effectively extracts the most significant information from facial images through dimensionality reduction, where KL Transform handles optimal feature extraction and PCA identifies the principal components representing maximum variance in the dataset. Key MATLAB functions utilized include pca() for principal component computation, cov() for covariance matrix calculation, and eig() for eigenvalue decomposition. The implementation involves preprocessing steps like image normalization, followed by feature vector projection onto the eigenface space created from the training dataset. This approach significantly enhances recognition accuracy by focusing on the most discriminative facial patterns while reducing noise and redundancy. The MATLAB environment facilitates efficient image processing operations using Image Processing Toolbox functions like imread() and imresize(), along with streamlined algorithm debugging through MATLAB's interactive development tools. This mini-program serves as a practical and effective solution suitable for various applications including security access control systems, facial payment verification, automated attendance tracking, and biometric authentication systems.