Gender classification from facial images using gray relational analysis with novel local binary pattern descriptors

Abstract

Gender classification (GC) is one of the major tasks in human identification that increase its accuracy. Local binary pattern (LBP) is a texture method that employed successfully. But LBP suffers a major problem; it cannot capture spatial relationships among local textures. Therefore, in order to increase the accuracy of GC, two LBP descriptors, which are based on (1) spatial relations between neighbors with a distance parameter, and (2) spatial relations between a reference pixel and its neighbor on the same orientation, were employed to extract features from facial images. Additionally, gray relational analysis (GRA) was carried out to identify gender through extracted features. Experiments on the FEI database illustrated the effectiveness of the proposed approaches. Achieved accuracies are 97.14, 93.33, and 92.50% by applying GRA with the nLBP$_{d}$, dLBP$_{\alpha }$, and traditional LBP features, respectively. Experimental results indicated that the proposed approaches were very competitive feature extraction methods in GC. Present work also showed that the nLBP$_{d}$, dLBP$_{\alpha }$ methods were obtained more acceptable results than traditional LBP.