Hand Gesture Recognition Based on an Improved Convex Shape Decomposition

Hand gesture recognition has become one of the most natural way of human communication with computer, However, the recognition accuracy and efficiency of the traditional methods still need to be improved. In this paper, a novel method was proposed based on Kinect for static gesture recognition in real time. Firstly, a simple and feasible hand detection and segmentation method was put forward based on depth information and skin color information. Then, a improved convex shape decomposition method was developed to obtain hand skeleton, and a gesture recognition method was put forward based on comparing the geodesic paths between skeleton endpoints. Finally, a comparative experiment was carried out for a specific gesture set. The experimental results show that, this method can provided a good performance in recognition accuracy and efficiency of the algorithm.