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Dynamic Bayesian Network based Two-Hand Gesture Recognition  

Suk, Heung-Il (부경대학교 컴퓨터공학과)
Sin, Bong-Kee (부경대학교 컴퓨터멀티미디어공학부)
Publication Information
Journal of KIISE:Software and Applications / v.35, no.4, 2008 , pp. 265-279 More about this Journal
Abstract
The idea of using hand gestures for human-computer interaction is not new and has been studied intensively during the last dorado with a significant amount of qualitative progress that, however, has been short of our expectations. This paper describes a dynamic Bayesian network or DBN based approach to both two-hand gestures and one-hand gestures. Unlike wired glove-based approaches, the success of camera-based methods depends greatly on the image processing and feature extraction results. So the proposed method of DBN-based inference is preceded by fail-safe steps of skin extraction and modeling, and motion tracking. Then a new gesture recognition model for a set of both one-hand and two-hand gestures is proposed based on the dynamic Bayesian network framework which makes it easy to represent the relationship among features and incorporate new information to a model. In an experiment with ten isolated gestures, we obtained the recognition rate upwards of 99.59% with cross validation. The proposed model and the related approach are believed to have a strong potential for successful applications to other related problems such as sign languages.
Keywords
Hands gesture recognition; hands tracking; dynamic Bayesian network; coupled hidden Markov model;
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