Face Recognition Program Implementing PCA for Dimensionality Reduction

This article presents a highly effective face recognition program that employs a two-stage algorithmic approach. The program first applies Principal Component Analysis (PCA) for dimensionality reduction, which involves computing eigenvectors from the covariance matrix of facial images to reduce feature space while preserving critical facial patterns. Subsequently, Linear Discriminant Analysis (LDA) is implemented for classification, optimizing class separability by maximizing between-class variance and minimizing within-class variance. The implementation utilizes the ORL face database, a widely recognized facial image dataset containing multiple facial variations across different subjects. Through the combined PCA-LDA methodology, the program achieves efficient face discrimination with robust recognition performance. In practical applications, the program may require additional optimization steps such as hyperparameter tuning for PCA components and LDA thresholds to adapt to diverse facial image conditions. However, the core framework provides an excellent foundation for face recognition systems, featuring modular code structure with separate functions for PCA transformation, LDA classification, and cross-validation procedures.