Local Binary Pattern, Local Derivative Pattern and Skeleton Features for RGB-D Person Re-identification

摘要

Novel methods based on depth and skeleton information of RGB-D sensors are proposed for person re-identification. Firstly, the depth images of the body are divided into three parts, i.e., head, torso and legs. Then, each part is described using histograms of local binary pattern and local derivative pattern. Also, the local pattern descriptors are combined with Gabor features for robustness against illumination. In the next step, these features are combined with skeleton features using the score-level fusion with sum rule. The results are evaluated on the KinectREID database, and experimental results show the good performance of the proposed methods.