Abstract:

Hand gesture recognition in complex background is susceptible to environmental interference, thus leading to recognition difficulty. According to this recognition problem, by analyzing the appearance features of hand gesture, a hand gesture recognition algorithm for natural human computer interaction is proposed and implemented. By using depth images which obtain from Kinect, we extract features like gesture finger radians, radians between fingers and the number of fingers, and properly utilize minimum distance algorithm for achieving fast and efficient classification. Experimental results show that the algorithm is robust and real-time with an average recognition rate of 94.3% for nine frequently-used gestures.

Key words: computer vision；depth image；finger radian；appearance features；gesture recognition