Discriminative binary pattern descriptor for face recognition

Abstract

Among several local descriptors invented in literature, the local binary pattern (LBP) is the prolific one. Despite its advantages like low computational complexity and monotonic gray invariance property, there are various demerits are observed in LBP and these are limited spatial patch, high dimension feature, noisy thresholding function and un-affective in harsh illumination variations. To overcome these issues presented work introduces the novel local descriptor called as discriminative binary pattern (DBP). Precisely two descriptors are introduced under DBP so-called Radial orthogonal binary pattern (ROBP) and radial variance binary pattern (RVBP). In former proposed descriptor, for neighborhood comparison, the center pixel is replaced by mean of medians computed from [orthogonal pixels + center pixel] of two 3 × 3 pixel window, formed from radius S1 and S2 of the 5 × 5 image patch. In latter proposed descriptor, the radial variances generated from 8 pair of two pixels are utilized for comparison with their mean value. In case of the both proposed descriptors, the sub-region wise histograms are extracted and fused to develop the entire feature size. Further the feature length of ROBP and RVBP are merged to form the size of the DBP descriptor. The compression is conducted by principal component analysis (PCA) and Fishers linear discriminant analysis). For matching support vector machines is used. Experiments conducted on 8 benchmark datasets reveals the effectiveness of the proposed DBP as compared to the other state of art benchmark methods.