Gabor-scale binary pattern for face recognition

In this paper, a novel face descriptor, the Gabor-scale binary pattern (GSBP), is proposed to explore the neighboring relationship in spatial, frequency and orientation domains for the purpose of face recognition. In order to extract the GSBP feature, the Gabor-scale volume and the Gabor-scale vector are introduced by using a group of Gabor wavelet coefficients with a special orientation. Moreover, the Gabor-scale length pattern and the Gabor-scale ratio pattern are proposed. Compared with the existed methods, GSBP utilizes the deep relations between neighboring Gabor subimages instead of directly combining Gabor wavelet transform and local binary pattern. For estimating the performance of GSBP, we compare the proposed method with the related methods on several popular face databases, including LFW, FERET, AR, Yale and Extended YaleB databases. The experimental results show that the proposed method outperforms several popular face recognition methods.