Face Recognition Using the Classic KL Transform Algorithm

This program implements face recognition using the classic KL (Karhunen-Loève) Transform algorithm, also known as Principal Component Analysis (PCA). The core implementation involves calculating eigenvectors from a training set of facial images to create an eigenface space, where recognition is performed by projecting test images into this space and finding the closest matching training sample. With minimal code adjustments—primarily modifying the input data preprocessing and feature extraction modules—this algorithm can be extended to general image classification and text pattern recognition tasks. The KL Transform's dimensionality reduction capabilities enable more accurate and efficient identification while improving practical application performance through optimized feature representation. Key functions typically include covariance matrix computation, eigenvalue decomposition, and distance metric implementation for matching comparisons.