Hand Gesture Recognition Using Multivariate Fuzzy Decision Tree and User Adaptation

Moon-Jin Jeon, Sang Wan Lee and Zeungnam Bien
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Moon-Jin Jeon: Korea Aerospace Research Institute, Korea
Sang Wan Lee: Massachusetts Institute of Technology, USA
Zeungnam Bien: Ulsan National Institute of Science and Technology, Korea

International Journal of Fuzzy System Applications (IJFSA), 2011, vol. 1, issue 3, 15-31

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.

Date: 2011
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