Sparse representation method based on Gabor and CLBP

Abstract

Sparse representation method (SRM) is a state-of-the-art face recognition method. Nevertheless, SRM exploits image samples rather than image features to perform classification. As we know, the proper feature of the image can be more discriminative than the image sample itself. For example, Gabor and local binary pattern (LBP), two kinds of widely used features, have shown excellent discriminative performance in face recognition. Recently a number of experiments have shown that complete local binary pattern (CLBP) obtains a much better result than LBP in recognizing the texture images. With this paper, we propose a novel sparse representation method based on Gabor and CLBP features for face recognition. Our method first extracts the most discriminative features and then uses SRM to perform face recognition. The proposed method is composed of the following steps: the first step is to perform the histogram equalization operation on the image samples. The second step extracts the Gabor and CLBP features from the image samples. The last step uses the sparse representation method based on the combination of Gabor and CLBP features to perform classification. The rationales of our method are as follows: the first step can reduce the adverse effects caused by the variable illuminations. Both of the Gabor and CLBP features not only are very discriminative but also are complementary. A large number of experiments show the superior performance of our method. For the Feret face database, the rate of classification error of our method is 28.8% lower than that of SRM and 14.8% lower than that of LRC. For the ORL face database, the rate of classification error of our method is 9% lower than that of SRM and 9.5% lower than that of LRC.