Resampling LDA/QR and PCA+LDA for Face Recognition

Principal Component Analysis (PCA) plus Linear Discriminant Analysis (LDA) (PCA+LDA) and LDA/QR are both two-stage methods that deal with the small sample size (SSS) problem in traditional LDA. When applied to face recognition under varying lighting conditions and different facial expressions, neither method may work robustly due to limited number of training samples for each class in the training set. Recently, resampling, a technique that generates multiple subsets of samples from the training set, has been successfully employed to improve the classification performance of the PCA+LDA classifier. In this paper, stimulated by such success, we propose a resampling LDA/QR method to improve LDA/QR’s performance. Furthermore, by analyzing the difference between LDA/QR and PCA+LDA and taking advantage of such difference, we incorporate LDA/QR and PCA+LDA in a combined framework by resampling for face recognition. Experimental results on AR dataset show that 1) resampling LDA/QR yields significantly higher classification performance than the original LDA/QR, and 2) resampling LDA/QR and resampling PCA+LDA in a combined framework further improves the classification compared to either resampling LDA/QR or resampling PCA+LDA.