MATLAB Source Code for Face Recognition Using PCA and Its Improved Algorithm MPCA

In this article, we present the application of Principal Component Analysis (PCA) and its improved algorithm Multilinear PCA (MPCA) for face recognition tasks. We provide detailed explanations of both algorithms' underlying principles and implementation methodologies. The implementation includes key MATLAB functions for data preprocessing, covariance matrix computation, eigenvalue decomposition, and projection matrix generation. For MPCA, we extend the approach to handle multimodal data through tensor decomposition techniques. Additionally, we supply complete MATLAB source code implementing both PCA and MPCA algorithms, featuring functions for training dataset processing, feature dimension reduction, and classification module integration. Through this article, you will understand the advantages and limitations of these algorithms in face recognition applications and gain practical experience in implementing them within the MATLAB environment for biometric recognition systems.