Dimensionality Reduction of Face Images using PCA

This program initially applies PCA (Principal Component Analysis) technique for dimensionality reduction of face images. PCA serves as a fundamental data compression method that significantly reduces image data dimensions, thereby improving classifier training efficiency. The implementation typically involves computing covariance matrices, eigenvalues, and eigenvectors to identify principal components that preserve maximum data variance. After completing the dimensionality reduction process, the program trains a neural network classifier using the reduced-dimensional data. Neural networks simulate human brain information processing systems and are widely applied in tasks like image classification and speech recognition. The MATLAB implementation likely utilizes the Neural Network Toolbox, where key functions like 'patternnet' or 'feedforwardnet' create multilayer perceptron architectures. Training involves backpropagation algorithms with optimization methods such as gradient descent or scaled conjugate gradient. Through neural network classifier training, the system can categorize different face images into distinct classes, enabling facial recognition functionality. This MATLAB-based program helps researchers better understand PCA applications, neural network classifier implementation, and the development of related facial recognition algorithms. The code structure probably includes separate modules for data preprocessing, PCA transformation, neural network configuration, and classification performance evaluation.