First-order Riesz transform based monogenic signal representation has been widely used in image processing and computer vision, however it only characterizes image intrinsic one-dimensional structure, and is incapable of describing intrinsic two-dimensional structure. To this end, a novel feature extraction approach, named Riesz Binary Pattern (RBP), is proposed for face recognition based on image multi-scale analysis and multi-order Riesz transform. RBP consists of two complementary components, i.e., local Riesz binary pattern (LRBP) and global Riesz binary pattern (GRBP). LRBP is obtained by performing local binary coding operator on each Riesz transform response to extract image intrinsic two-dimensional structure features. While GRBP is the global binary coding of joint information of image pixel multi-scale analysis and multi-order Riesz transform. Histogram of LRBP and GRBP are concatenated to form face image RBP description. Experimental results on three databases demonstrate that our proposed RBP descriptor is more discriminant in extracting image information and can provide a higher classification rate compared to some state-of-the-art image representation methods. 
Face recognition with Riesz binary pattern