A Virtual Mouse interface based on Two-layered Bayesian Network

Myung-Cheol Roh, Sungju Huh, Seong-Whan Lee

Abstract: Recently, many studies on gestural control methods for substituting for keyboard and mouse devices have been conducted because of their conveniences and intuitiveness. This paper presents a virtual mouse interface which is a gesture-based mouse interface and two-layered Bayesian network (TBN) for robust hand gesture recognition in real-time. The TBN provides robust recognition of hand gestures, as it compensates for an incorrectly recognized hand posture and its location via the preceding and following information. Experiments demonstrate that the proposed model recognizes hand gestures with a recognition rate of 93.78% and 85.15% for a simple and cluttered background, respectively.