The Behavior of Principal Component Analysis and Linear Discriminant Analysis (PCA-LDA) for Face Recognition

This paper presents the analysis of PCA-LDA behavior for face recognition using Singular Value Decomposition (SVD). The experimental results is shown to analyze face recognition performance, i.e. the impact of number of subjects, images per subject, training set size, and trade-off between the number of subjects and the number of images per subject on recognition performance, in relation with the number of PCA-LDA coefficients. The comparison of three classifiers, i.e. Euclidean Distance, Cosine Similarity, and Likelihood Ratio, are presented to obtain knowledge about their characteristics. All experimental evaluations are in the verification context. Based on the experimental results, the larger number of subjects and images per subject produced the better recognition performance. Regarding the number of subjects and images per subject trade-off, its indicated both of them influence the recognition performance. Otherwise, the image size also affect to recognition performance. PCA-LDA can perform low resolution image well up to 15x15 pixels and breaks down afterward. Regarding the p and ` coefficients, PCA-LDA has different behavior for each classifier.