• KIISE Transactions on Computing Practices
• /
• v.21 no.8
• /
• pp.549-554
• /
• 2015

As intuitive hardware interfaces continue to be developed, it has become more important to recognize the posture of the user. An efficient alternative to adding expensive sensors is to implement computer vision systems. This paper proposes a method to recognize a user's postured in a live broadcast bi-directional participatory TV program. The proposed method first estimates the position of the user's hands by generation a facial color map for the user and a motion map. The posture is then recognized by computing the relative position of the face and the hands. This method exhibited 90% accuracy in an experiment to recognize three defined postures during the live broadcast bi-directional participatory TV program, even when the input images contained a complex background.

• Journal of KIISE:Software and Applications
• /
• v.36 no.12
• /
• pp.1028-1033
• /
• 2009

This paper presents a real-time hand gesture recognition approach for controlling a computer. We define hand gestures as continuous hand postures and their movements for easy expression of various gestures and propose a Two-layered Bayesian Network (TBN) to recognize those gestures. The proposed method can compensate an incorrectly recognized hand posture and its location via the preceding and following information. In order to vertify the usefulness of the proposed method, we implemented a Virtual Mouse interface, the gesture-based interface of a physical mouse device. In experiments, the proposed method showed a recognition rate of 94.8% and 88.1% for a simple and cluttered background, respectively. This outperforms the previous HMM-based method, which had results of 92.4% and 83.3%, respectively, under the same conditions.