Face recognition based on discriminative dictionary with multilevel feature fusion

Abstract

In order to alleviate the influence of illumination, pose, expression and occlusion variations in face recognition, in this paper, an effective face recognition method based on discriminative sparse representation is proposed. To solve the problem of these variations, we extract discriminative features which represent for each of the training images, and propose a novel dictionary by learning discriminative features. Firstly, we decompose a test image by using nonsubsampled contourlet transform (NSCT), and then fuse the information according to the features from each subband and their contributions. Finally, we obtain the discriminative features of training images and construct a discriminative dictionary. Fuse these multiple features can improve the efficiency and effectiveness of face recognition, especially when training samples are limited and the dimension of feature vector is low. Experimental results on two widely used face databases are presented to demonstrate the efficiency of the proposed approach.