2D LDA+PCA Face Recognition MATLAB Implementation

This document presents a MATLAB implementation of 2D LDA+PCA face recognition that can be used directly without any modifications. The program is designed with a complete workflow including data preprocessing, feature extraction, and classification modules.

The implementation combines two renowned algorithms: Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA). LDA, a supervised learning algorithm, performs dimensionality reduction while maximizing between-class separation and minimizing within-class variance. PCA, an unsupervised technique, projects high-dimensional data onto a lower-dimensional space through linear transformation to capture maximum variance. The code implements PCA first for initial dimensionality reduction, followed by LDA for optimal class separation, creating an efficient pipeline for facial feature extraction.

Before using this program, it's recommended to have fundamental understanding of both LDA and PCA algorithms to better comprehend the implementation logic. The MATLAB code includes clearly defined functions for covariance matrix calculation, eigenvalue decomposition, and projection matrix computation. Users can customize the program by adjusting parameters such as the number of principal components, regularization terms, and classification thresholds to meet specific requirements. The implementation supports standard image formats and includes sample datasets for immediate testing.