Face Recognition and Classification Using 2D-PCA Algorithm

This document presents a face recognition and classification program based on the 2D-PCA (Two-Dimensional Principal Component Analysis) algorithm, designed to identify and categorize facial images from the FACE-ORL face database. The program demonstrates practical utility across various domains including security surveillance and biometric authentication systems. Students and researchers can leverage this implementation to study and explore face recognition technologies through hands-on experimentation. Key technical features include: - Complete integration of the FACE-ORL database with standardized image preprocessing - Efficient 2D-PCA implementation for direct covariance matrix computation from image matrices - Facial feature extraction through eigenvector projection and dimensionality reduction - Visualization components for displaying original images, eigenfaces, and classification results - Modular code structure allowing customization of parameters like retained variance ratio and k-nearest neighbor classification The program employs matrix-based operations that preserve spatial relationships in facial images, contrasting with traditional 1D-PCA approaches that require vectorization. The algorithm calculates covariance matrices directly from 2D image arrays, improving computational efficiency while maintaining recognition accuracy. Classification utilizes distance metrics in the reduced feature space for pattern matching. This implementation serves as both an educational resource for understanding multivariate statistical pattern recognition and a practical foundation for developing advanced computer vision applications, enabling users to deepen their comprehension and application of modern face recognition methodologies.