Dynamic gesture recognition by directional pulse coupled neural networks for human-robot interaction in real time

Abstract

Dynamic gesture recognition is a major topic for most real-time human-robot interaction applications. Recently, increasingly people have focused on dynamic gesture recognition based on 3D representation. In this paper, the gesture recognition is converted into the shortest path problem by transforming the feature matrix to an undirected graph and a novel dynamic gesture recognition algorithm of directional pulse coupled neuron network (DPCNN) is proposed for real time human-robot interactions. The DPCNN can select the firing direction by giving different excitations to neighbor neurons and reduce the effects of useless neurons. Furthermore, to reduce the recognition time, an early gesture recognition method based on the adaptive window is introduced to recognize the unfinished gestures. DPCNN reduces the computation time and achieves a high recognition rate on three public datasets compared with other algorithms which improves the efficiency of real-time dynamic gesture recognition and ensures a friendly experience for human-robot interactions.