PCA+Fisher Face Recognition MATLAB Implementation

This document presents a comprehensive MATLAB implementation of PCA+Fisher face recognition methodology. The package includes a complete image database specifically designed for testing and validating facial recognition algorithms. Principal Component Analysis (PCA) serves as the foundational dimensionality reduction technique in this implementation, which computationally transforms high-dimensional facial data into a lower-dimensional feature space while preserving maximum variance. The MATLAB code efficiently calculates eigenvectors and eigenvalues from the covariance matrix of training images, typically using built-in functions like 'pca' or 'eig' for optimal performance. The Fisher Linear Discriminant method complements PCA by implementing class separation optimization. The algorithm computes between-class and within-class scatter matrices to determine the optimal projection direction that maximizes class separability. Key functions in the MATLAB implementation include scatter matrix calculations and eigenvalue decomposition for discriminant feature extraction. The integrated approach utilizes PCA for initial feature reduction followed by Fisher's method for enhanced classification performance. The accompanying image database contains pre-processed facial images organized in training and testing sets, allowing users to validate the algorithm's accuracy through cross-validation techniques. This implementation demonstrates practical MATLAB programming techniques for image processing, including image normalization, feature extraction, and classification algorithms. Users can modify parameters such as the number of principal components and discriminant thresholds to optimize recognition performance for different scenarios. The code structure follows modular design principles, separating data preprocessing, training, and testing phases for clear understanding and easy customization.