Dynamic Hand Gesture Trajectory Recognition Based on Block Feature and Skin-Color Clustering

Abstract

In recent years, dynamic hand gesture recognition has been a research hotspot of human-computer interaction. Since most existing algorithms contain problems with high computational complexity, poor real-time performance and low recognition rate, which cannot satisfy the need of many practical applications. Moreover, key frames obtained by inter-frame difference degree algorithm contain less information, which leads to less identified species and lower recognition rate. To solve these problems, we present a dynamic hand gesture trajectory recognition method based on the theory of block feature to extract key frames and the skin-color clustering’s hand gesture segmentation. Firstly, this method extracts block feature of degree of difference between frames in hand gesture sequence to select key frames accurately. Secondly, the method based on skin-color clustering is applied to obtain the area of hand gesture after segmenting hand gestures from images. Finally, hidden Markov model (HMM), in which the angle data of hand gesture trajectories are input, is used for modeling and identifying dynamic hand gestures. Experimental results show that the method of key-frame extraction is used to obtain information of dynamic hand gestures accurately, which would improve the recognition rate of dynamic hand gesture recognition and, at the same time, can guarantee the real-time of hand gesture recognition system. The average recognition rate is up to 86.67%, and the average time efficiency is 0.39s.