MATLAB Implementation for ORL Face Database

PCA (Principal Component Analysis) is a classical face recognition method that extracts essential data features through dimensionality reduction, widely applied to face databases like ORL. The ORL face database contains 400 grayscale images of 40 subjects, with 10 images per person capturing different poses and expressions, making it one of the standard datasets for testing face recognition algorithms. Implementing PCA-based face recognition in MATLAB involves the following key steps: First, convert the ORL images into column vectors and combine them into a data matrix. Calculate the mean face and center all samples by subtracting this mean. Next, compute eigenvalues and eigenvectors either through the covariance matrix or SVD decomposition. Select the top-k eigenvectors corresponding to the largest eigenvalues to construct the projection subspace (i.e., the eigenface space). During testing, project the input image onto this subspace and perform nearest-neighbor matching (e.g., using Euclidean distance) with the projected training set results to complete classification. The core of the eigenface method lies in representing primary facial variation patterns using low-dimensional orthogonal bases. MATLAB's matrix computation advantages (e.g., eig()/svd() functions) enable efficient eigenvalue decomposition. Practical considerations include data normalization, determining the number of principal components (typically retaining >90% energy), and strict separation of training and test sets. This method shows sensitivity to lighting and pose variations, which can be optimized by integrating LDA or deep learning approaches. Implementation pointers: - Use imread() to load images and reshape() to convert them to column vectors - Apply mean() for centering and cov() for covariance matrix calculation - Leverage svd() for stable eigenvalue decomposition when handling high-dimensional data - Implement knnsearch() or custom distance calculation for nearest-neighbor classification - Consider pca() function as an alternative for streamlined dimensionality reduction