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 real-world 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.