Face Recognition Using Eigenfaces with Machine Learning Implementation

This documentation presents a complete source code implementation for face recognition utilizing the eigenfaces approach. Eigenfaces represent a sophisticated computer vision technique that transforms facial images into numerical feature vectors and extracts distinctive facial characteristics. These feature vectors enable systematic comparison between different facial images and facilitate similarity measurement for identification purposes. Our implementation incorporates fundamental machine learning algorithms including Principal Component Analysis (PCA) for dimensionality reduction and feature extraction, followed by Support Vector Machines (SVM) for robust classification. The PCA implementation efficiently reduces image dimensionality while preserving the most significant facial features, converting high-dimensional image data into compact eigenface representations. The SVM classifier then leverages these extracted features to perform accurate face recognition with optimized decision boundaries. We provide extensive documentation that thoroughly explains the code architecture, algorithm workflow, and key function implementations. The documentation covers image preprocessing techniques, covariance matrix computation, eigenvalue decomposition processes, and classification mechanism details. This comprehensive guide enables researchers, developers, and enthusiasts to understand the underlying principles, modify the codebase according to specific requirements, and adapt the implementation for various applications. The codebase includes modular functions for: - Image normalization and grayscale conversion - Eigenvalue decomposition and eigenvector calculation - Feature vector projection and similarity scoring - Model training and validation procedures - Real-time recognition capabilities Whether you are conducting academic research, developing commercial applications, or exploring computer vision technologies, this implementation provides a solid foundation for face recognition projects and can be readily customized to meet specific operational requirements.