MATLAB Implementation of 2DPCA Algorithm with Face Recognition Application

I have implemented the 2DPCA (Two-Dimensional Principal Component Analysis) algorithm using MATLAB, specifically designed and tested on the ORL face database. This implementation achieves high face recognition rates by effectively extracting discriminative features from facial images. The 2DPCA algorithm serves as a powerful method for dimensionality reduction and feature extraction, directly processing 2D image matrices without requiring vectorization. This approach preserves the spatial structure of facial images while extracting the most significant features for recognition and classification tasks. The implementation involves computing the image covariance matrix directly from the 2D data and extracting eigenvectors to form a projection matrix. Key implementation aspects include: - Direct processing of 2D face images (112×92 pixels from ORL database) - Efficient covariance matrix computation using matrix operations - Eigenvalue decomposition to obtain optimal projection vectors - Feature extraction through image projection onto the eigenvector space - Nearest neighbor classification for recognition matching Through comprehensive testing on the ORL face database, this algorithm demonstrates excellent performance in face recognition applications, showing high accuracy and reliability. The implementation confirms that 2DPCA effectively captures essential facial features while reducing computational complexity compared to conventional PCA methods. This algorithm holds significant potential for widespread application in face recognition systems, particularly due to its computational efficiency and robust performance in feature extraction from facial image data.