Face Recognition using PCA, LDA, and BP Neural Network with GUI Implementation

This project implements a sophisticated face recognition system combining Principal Component Analysis (PCA) for dimensionality reduction, Linear Discriminant Analysis (LDA) for feature discrimination enhancement, and Backpropagation (BP) Neural Network for classification. The MATLAB implementation includes a fully functional Graphical User Interface (GUI) developed and tested on MATLAB 8.0 platform. The system workflow begins with PCA processing to extract principal components and reduce feature dimensions, followed by LDA optimization to maximize class separability. The refined features are then fed into a BP neural network trained with error backpropagation algorithm for accurate face classification. The GUI implementation provides intuitive controls for dataset loading, parameter configuration, training execution, and recognition testing. Key implementation aspects include: - PCA module using eigenvalue decomposition for feature extraction - LDA algorithm with between-class and within-class scatter matrix computation - BP neural network with configurable hidden layers and activation functions - Modular code structure allowing independent testing of each component - Comprehensive error handling and progress visualization This robust implementation has been validated for practical use and serves as an excellent reference for academic projects, particularly suitable for graduation design requirements with its complete documentation and reproducible results.