Face Recognition Using KL Transformation

This program implements face recognition using Karhunen-Loève (KL) transformation. The implementation leverages the ORL face database, which contains a compressed archive of various facial images. The KL transformation, also known as Principal Component Analysis (PCA), is employed to extract essential facial features by reducing dimensionality while preserving critical pattern information. Key implementation aspects include preprocessing face images, computing covariance matrices, deriving eigenvectors for the feature space, and performing classification using distance metrics. The algorithm effectively handles facial variations by projecting images onto the eigenface subspace. Additionally, a comprehensive Word-format experiment report is provided, detailing the complete experimental procedure, parameter configurations, and performance evaluation metrics. The report thoroughly documents the implementation workflow, from data preprocessing to recognition results, demonstrating excellent experimental outcomes with high accuracy rates. The code includes modules for data loading, feature extraction, and classification, making it suitable for both educational and research purposes.