Hand gesture recognition that has proven a significant factor to directly influence the nonverbal communication between human and computer is becoming a challenging topic in the field of machine vision. This paper aims to propose a novel hand gesture recognition system which applies sparse representation to the Kinect to improve the efficiency of Kinect-based human-computer interaction. Auto-encoder neural network computation is also utilized to achieve better result. The sparse auto-encoder neural network is versatile and robust in complex features learning and computational efficient. Finally, results indicate that our algorithm greatly facilitates the gesture recognition rate up to 95%.
With the rapid development of the lowcost Microsoft Kinect, hand segmentation has been a resurgence of broad interest. This is because the depth and skeleton information provided by the Kinect opens an innovative way for hand segmentation. In this paper, we propose a new scheme for hand detection and segmentation based on depth and skeleton information. We conduct experiments on our new collect RGB and depth image pairs. The results demonstrate the robustness and effectiveness of our proposed model.
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