Hand gesture recognition using hidden Markov models

Hand gesture recognition from visual images has a number of potential application in HCl (human computer interaction), machine vision, VR(virtual reality), machine control in the industry field, and so on. Most conventional approaches to hand gesture recognition have employed datagloves. But, for more natural interface, hand gesture must be recognized from visual images as in the communication between humans without using any external devices. Our research is intended to draw and edit graphic elements by hand gesture. Up to now; many methods for hand gesture recognition have been proposed such as syntactical analysis, neural based approach, HMM (hidden Markov model) based recognition. As gesture is the continuous motion on the sequential time series, HMM must be a prominent recognition fool. Though each analysis method has merits and demerits, the most important thing in hand gesture recognition is what the input features are that represent very well the characteristics of moving hand gesture. In our research, we consider the planar hand gesture in front of camera and therefore 8-directional chain codes as input vectors. For training an HMM network, a simple context modeling method is embedded as training on ''left-to-right'' HMM model. This model is applied to draw, graphic elements such as triangle, rectangular, circle, are, horizontal line, vertical line and edit the specified graphic elements such as copy, delete, move, swap, undo, close. Therefore, the overall objectives are 12 dynamic gestures. In our experiments, we have good recognition results on a pre-confined test environment : 1) the spotting time is synchronized at the static state of a hand, 2) other limb parts except hands is motionless, 3) the change of hand posture during movement is meaningless. Our system will be advanced by adopting more diverse input features representing well dynamic features of hand gestures.