MATLAB Source Code for Face Recognition Using Principal Component Analysis

Principal Component Analysis (PCA) is a classic dimensionality reduction method widely applied in face recognition systems. Through PCA, high-dimensional face image data can be mapped to a lower-dimensional feature space, extracting the most discriminative feature components. The implementation approach typically utilizes Singular Value Decomposition (SVD). In MATLAB code, this involves preprocessing training set face images through operations like grayscale conversion and normalization using functions such as rgb2gray() and imresize(). Subsequently, the average face is computed from all training samples using mean() function, and each face image is centered by subtracting this average face. The covariance matrix is then constructed, and eigenvectors are obtained through SVD decomposition using MATLAB's svd() function. These eigenvectors represent the "eigenfaces" - the principal components that capture maximum variance in the data. The code typically sorts these eigenvectors by their corresponding eigenvalues to select the most significant components. During the recognition phase, test faces are projected onto the feature space spanned by the principal components. Classification is performed by comparing coordinate distances in this feature space between test samples and training samples. Distance metrics commonly implemented include Euclidean distance (using norm() or pdist2() functions) or cosine similarity. Classifiers like k-nearest neighbors (fitcknn()) or Support Vector Machines (fitcsvm()) can be integrated for final decision making. Although PCA is efficient and straightforward, it exhibits sensitivity to variations in lighting conditions and facial expressions. The code structure allows for subsequent integration with methods like Linear Discriminant Analysis (LDA) or deep learning approaches to enhance recognition performance, potentially through MATLAB's Classification Learner app or Neural Network Toolbox.