A gesture recognition system with retina-V1 model and one-pass dynamic programming

Abstract

Dynamic Programming (DP) algorithm has been studied from 1940s and successfully applied to pattern recognition fields such as continuous speech recognition, hand writing recognition, gesture recognition and so on. In this paper, we propose a novel hand gesture recognition system which includes three kinds of image processing: skin area segmentation, motion estimation by a retina-V1 model, and a gesture discrimination algorithm of One-Pass Dynamic Programming (One-Pass DP). A HSV-RGB filter is used to extract skin area in the color image, and the simple motion of hand area is estimated in eight directions by a retina-V1 model which is a computational model of primary visual cortex. Then the motions are used to compose 40 basic templates of gestures. In other words, hand gestures are considered as combinations of templates of simple motions, and One-Pass DP is used to recognize the pattern of gestures. Experiments dealt with individual and compound gestures were executed by online processing, and the results confirmed the effectiveness of the proposed system.