PCA+LDA Face Recognition with Enhanced Feature Dimensionality Reduction

In the field of face recognition, PCA and LDA are two widely used algorithms. PCA extracts principal features from images through eigenvalue decomposition of covariance matrices, while LDA identifies the most discriminative features by maximizing between-class variance and minimizing within-class variance. Recent research demonstrates that combining PCA and LDA significantly improves recognition rates, particularly for large-scale datasets. To implement this hybrid approach, the MATLAB Dimensionality Reduction Toolbox is essential for feature dimension reduction. The toolbox provides functions like pcacov() and lda() that transform high-dimensional feature vectors into lower-dimensional representations through linear projections, enabling more effective application of both PCA and LDA algorithms. The implementation typically involves preprocessing steps like image normalization, followed by PCA dimensionality reduction using singular value decomposition (SVD), with LDA then applied to the reduced feature space for optimal class separation.