Hand posture recognition in gesture-based human-robot interaction

Natural and friendly interface is critical for the development of service robots. Gesture-based interface offers a way to enable untrained users to interact with robots more easily and efficiently. In this paper, we present a posture recognition system implemented on a real humanoid service robot. The system applies the RCE neural network based color segmentation algorithm to separate hand images from complex backgrounds. The topological features of the hand are then extracted from the silhouette of the segmented hand region. Based on the analysis of these simple but distinctive features, hand postures are identified accurately. Experimental results on gesture-based robot programming demonstrated the effectiveness and robustness of the system.