PCA for Face Recognition Implementation

This program implements face recognition using Principal Component Analysis (PCA), a classical dimensionality reduction technique that reduces feature count while improving computational efficiency. The implementation includes detailed inline comments explaining each processing stage - from data preprocessing and covariance matrix calculation to eigenvalue decomposition and feature projection. The code demonstrates how to extract principal components representing the most significant facial variations and construct eigenfaces for classification. The program structure allows for easy extension to explore alternative dimensionality reduction methods such as LDA (Linear Discriminant Analysis) for supervised feature extraction and t-SNE (t-Distributed Stochastic Neighbor Embedding) for nonlinear dimensionality reduction. Through comparative analysis of different algorithms, users can gain deeper insights into facial recognition principles and optimize feature representation strategies for improved accuracy.