Compact LBP and WLBP Descriptor With Magnitude and Direction Difference for Face Recognition
Journal
Proceedings - International Conference on Image Processing, ICIP
Journal Volume
2017-September
Pages
1067-1071
Date Issued
2017
Author(s)
SOO-CHANG PEI
Mei-Shuo Chen
Yi Yu
Suhua Tang
Chunlin Zhong
Abstract
In 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.
Subjects
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
Other Subjects
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
Type
conference paper