RLBP: Robust Local Binary Pattern

Abstract

In this paper, we propose a simple and robust local descriptor, called the robust local binary pattern (RLBP). The local binary pattern (LBP) works very successfully in many domains, such as texture classification, human detection and face recognition. However, an issue of LBP is that it is not so robust to the noise present in the image. We improve the robustness of LBP by changing the coding bit of LBP. Experimental results on the Brodatz and UIUC texture databases show that RLBP impressively outperforms the other widely used descriptors (e.g., SIFT, Gabor, MR8 and LBP) and other variants of LBP (e.g., completed LBP), especially when we add noise in the images. In addition, experimental results on human face recognition also show a promising performance comparable to the best known results on the Face Recognition Grand Challenge (FRGC) face dataset.