Abstract
In this paper, we propose a local descriptor, called Weber Local Binary Pattern (WLBP11WLBP is the abbreviation of Weber Local Binary Pattern. ), which effectively combines the advantages of WLD and LBP. Specifically, WLBP consists of two components: differential excitation and LBP. The differential excitation extracts perception features by Weber's law, while the LBP (Local Binary Pattern) can describe local features splendidly. By computing the two components, we obtain wo images: differential excitation image and LBP image, from which a WLBP histogram is constructed. The differential excitation was extended by bringing in Laplacian of Gaussian (LoG), which makes WLBP robust to noise. By designing a new quantization method, the discriminabilty of WLBP was enhanced. The proposed method is evaluated on the face recognition problem under different challenges. Experimental results show that WLBP performs better than WLD and LBP. Meanwhile, it is robust to time, facial expressions, lightings, pose and noise. We also conduct experiments on Brodatz and KTH-TIPS2-a texture databases, which demonstrate that WLBP is a powerful texture descriptor.
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