MATLAB Source Code for Face Recognition Using SVD-Based Principal Component Extraction

This MATLAB source code implements a face recognition system using Singular Value Decomposition (SVD) for principal component extraction. The core algorithm performs dimensional reduction by decomposing facial image matrices into orthogonal components, where dominant singular values correspond to the most significant facial features. The implementation includes functions for: preprocessing image datasets, computing covariance matrices, performing SVD decomposition, and calculating feature vectors for classification. This codebase demonstrates practical application of linear algebra concepts in computer vision, allowing users to train recognition models and test accuracy against input images. Users can modify parameters like the number of retained singular values to balance recognition accuracy and computational efficiency. The implementation serves as both a working recognition system and an educational resource for understanding fundamental machine learning techniques in biometric authentication.