Face Recognition using Linear Discriminant Analysis

This project implements face recognition using Linear Discriminant Analysis (LDA). The program has been experimentally validated on the ORL face database to evaluate the recognition accuracy of the LDA algorithm. The implementation includes key computational steps such as scatter matrix calculation, eigenvalue decomposition, and projection vector optimization to maximize class separability. Beyond the ORL database, this algorithm can be effectively applied to other facial recognition domains including security surveillance systems and identity verification applications. Through comprehensive testing across diverse datasets, the robustness and accuracy of the algorithm can be further validated. The code structure allows for parameter tuning and feature selection optimization to enhance recognition performance. Key functions include data preprocessing, dimensionality reduction, and classification module implementation. The algorithm demonstrates significant application potential and research value, with the codebase supporting extensions like kernel LDA implementations and real-time processing adaptations for practical deployment scenarios.