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Hand Gesture Recognition Using Multivariate Fuzzy Decision Tree and User Adaptation
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Author(s): Moon-Jin Jeon (Korea Aerospace Research Institute, Korea), Sang Wan Lee (Massachusetts Institute of Technology, USA)and Zeungnam Bien (Ulsan National Institute of Science and Technology, Korea)
Copyright: 2013
Pages: 15
Source title:
Contemporary Theory and Pragmatic Approaches in Fuzzy Computing Utilization
Source Author(s)/Editor(s): Toly Chen (Feng Chia University, Taiwan)
DOI: 10.4018/978-1-4666-1870-1.ch008
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Abstract
As an emerging human-computer interaction (HCI) technology, recognition of human hand gesture is considered a very powerful means for human intention reading. To construct a system with a reliable and robust hand gesture recognition algorithm, it is necessary to resolve several major difficulties of hand gesture recognition, such as inter-person variation, intra-person variation, and false positive error caused by meaningless hand gestures. This paper proposes a learning algorithm and also a classification technique, based on multivariate fuzzy decision tree (MFDT). Efficient control of a fuzzified decision boundary in the MFDT leads to reduction of intra-person variation, while proper selection of a user dependent (UD) recognition model contributes to minimization of inter-person variation. The proposed method is tested first by using two benchmark data sets in UCI Machine Learning Repository and then by a hand gesture data set obtained from 10 people for 15 days. The experimental results show a discernibly enhanced classification performance as well as user adaptation capability of the proposed algorithm.
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