SOO-CHANG PEIMei-Shuo ChenYi YuSuhua TangChunlin Zhong2019-10-242019-10-24201715224880https://scholars.lib.ntu.edu.tw/handle/123456789/428089https://www.scopus.com/inward/record.uri?eid=2-s2.0-85045303677&doi=10.1109%2fICIP.2017.8296445&partnerID=40&md5=d25df20f58aa1bef18dccc905f9cbc22In this paper, we propose a novel descriptor for face recognition on grayscale images, depth images and 2D+depth images. It is a compact and effective descriptor computed from the magnitude and the direction difference. It can be concatenated with conventional descriptors such as well-known Local Binary Pattern (LBP) and Weber Local Binary Pattern (WLBP), to enhance their discrimination capability. To evaluate the performance of our descriptor, we conducted extensive experiments on three types of images using four different databases. The experimental results demonstrate the robustness and superiority of our approach, and the performances of our new descriptor surpass that without magnitude and direction difference. At the end, we further compare our descriptor with Convolution Neural Network (CNN) to show the compactness and effectiveness of the proposed approach. © 2017 IEEE.Convolution; Convolution neural network; Depth image; Descriptors; Gray-scale images; Local binary pattern (LBP); Local binary patterns; Local descriptors; Magnitude and Direction Difference; Face recognition[SDGs]SDG10Convolution; Convolution neural network; Depth image; Descriptors; Gray-scale images; Local binary pattern (LBP); Local binary patterns; Local descriptors; Magnitude and Direction Difference; Face recognitionconference paper10.1109/icip.2017.82964452-s2.0-85045303677