Volume-based human re-identification with RGB-D cameras

This paper presents an RGB-D based human re-identification approach using novel biometrics features from the body's volume. Existing work based on RGB images or skeleton features have some limitations for realworld robotic applications, most notably in dealing with occlusions and orientation of the user. Here, we propose novel features that allow performing re-identification when the person is facing side/backward or the person is partially occluded. The proposed approach has been tested for various scenarios including different views, occlusion and the public BIWI RGBD-ID dataset.