2DPCA Algorithm for Face Recognition with MATLAB Implementation

This MATLAB program implements face recognition using the 2DPCA (Two-Dimensional Principal Component Analysis) algorithm combined with a nearest neighbor classifier. The system performs recognition by analyzing and comparing facial features extracted from images. The 2DPCA algorithm, a widely-used image recognition technique, efficiently extracts primary feature information from facial images through direct processing of image matrices without requiring vectorization. This approach preserves spatial relationships and reduces computational complexity compared to conventional PCA. The implementation includes covariance matrix calculation from 2D image arrays and eigenvector extraction for optimal feature projection. The nearest neighbor classifier operates by measuring similarity between the input face image and stored templates in the feature space. The algorithm computes Euclidean distances between feature vectors and identifies the closest match from the training dataset. This classifier provides a straightforward yet effective classification mechanism suitable for face recognition tasks. This program enables accurate and rapid face recognition, offering a practical tool for various applications including security systems, biometric authentication, and image analysis. The code structure includes modules for data preprocessing, feature extraction using 2DPCA, distance calculation, and classification decision-making.