Human Detection and Face Recognition Using 3D Structure of Head and Face Surfaces Detected by RGB-D Sensor

Summary of proposed method Home service robots must possess the ability to communicate with humans, for which human detection and recognition methods are particularly important. This paper proposes methods for human detection and face recognition that are based on image processing, and are suitable for home service robots. For the human detection method, we combine the method proposed by Xia et al. based on the use of head shape with the results of region segmentation based on depth information, and use the positional relations of the detected points. We obtained a detection rate of 98.1% when the method was evaluated for various postures and facing directions. We demonstrate the robustness of the proposed method against postural changes such as stretching the arms, resting the chin on one’s hands, and drinking beverages. For the human recognition method, we combine the elastic bunch graph matching method proposed by Wiskott et al. with Face Tracking SDK to extract facial feature points, and use the 3D information in the deformation computation; we obtained a recognition rate of 93.6% during evaluation.