A virtual mouse interface with a two-layered Bayesian network

During the last decade, many natural interaction methods between human and computer have been introduced. They were developed for substitutions of keyboard and mouse devices so that they provide convenient interfaces. Recently, many studies on vision based gestural control methods for Human-Computer Interaction (HCI) have been attracted attention because of their convenience and simpleness. Two of the key issues in these kinds of interfaces are robustness and real-time processing. This paper presents a hand gesture based virtual mouse interface and Two-layer Bayesian Network (TBN) for robust hand gesture recognition in real-time. The TBN provides an efficient framework to infer hand postures and gestures not only from information at the current time frame, but also from the preceding and following information, so that it compensates for erroneous postures and its locations under cluttered background environment. Experiments demonstrated that the proposed model recognized hand gestures with a recognition rate of 93.76 % and 85.15 % on simple and cluttered background video data, respectively, and outperformed previous methods: Hidden Markov Model (HMM), Finite State Machine (FSM).