PCA Dimensionality Reduction Followed by LDA Classification for Face Recognition

This documentation presents two essential modules: PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis), specifically implemented for face recognition systems. The implementation workflow first applies PCA to reduce data dimensionality by identifying principal components that capture maximum variance in facial features - typically achieved through eigenvalue decomposition of the covariance matrix. Subsequently, LDA performs classification by projecting the dimensionally-reduced data into a subspace that maximizes between-class separation while minimizing within-class variance, using scatter matrix calculations and Fisher's criterion optimization. Our experimental results demonstrate outstanding performance in face recognition accuracy. Notably, this PCA-LDA pipeline architecture exhibits transferability to other pattern recognition domains such as speech recognition and general image classification tasks, where similar feature extraction and classification challenges exist.