MATLAB Implementation of Face Recognition Using 2DPCA

This MATLAB implementation demonstrates face recognition using 2D Principal Component Analysis (2DPCA). The code performs face recognition by analyzing principal features of facial images through 2DPCA methodology, which directly operates on 2D image matrices without requiring vectorization. Key implementation aspects include: - 2DPCA feature extraction that computes covariance matrices directly from 2D image arrays - Eigenvector calculation for dimension reduction while preserving critical facial patterns - Pattern matching mechanism comparing test images with projected training samples - Matrix-based operations that maintain spatial relationships in facial features The algorithm works by constructing a feature space from training images, projecting new images into this space, and identifying matches through distance measurements. This approach provides computational efficiency compared to conventional PCA by avoiding image-to-vector transformations. Through this code, you can learn how to implement 2DPCA-based face recognition algorithms in MATLAB and adapt them for your own projects. Before using this code, ensure you have fundamental knowledge of MATLAB programming and understand basic principles of face recognition and dimensionality reduction techniques.