Fuzzy 2D Linear Discriminant Analysis Based on Sub-image and Random Sampling for Face Recognition

Abstract

Face recognition, as a research hot topic, still faces many challenges. This paper proposes a new face recognition method by fusing the advantages of fuzzy set theory, sub-image method and random sampling technique. In this method, we partition an original image into some sub-images to improve the robustness to different facial variations, and extract local features from each sub-image by using fuzzy 2D-Linear Discriminant analyzis (LDA) which makes use of the class information hidden in neighbor samples. In order to increase the diversity of component classifiers and retain as much as the structural information of the row vectors, we further randomly sample row vectors from each sub-image before performing fuzzy 2D-LDA. Experimental results on Yale A, ORL, AR and Extended Yale B face databases show its superiority to other related state-of-the-art methods on the different variations such as illumination, occlusion and facial expression. Furthermore, we analyze the diversity of our proposed method by virtue of Kappa diversity-error analyzis and frequency histogram and results show that the proposed method can construct more diverse component classifiers than other methods.