Resampling LDA/QR and PCA+LDA for Face Recognition

Abstract

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. 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, taking advantage of the difference between LDA/QR and PCA+LDA, we incorporate them by resampling for face recognition. Experimental results on AR dataset verify the effectiveness of the proposed methods.