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Hand Gesture Recognition Using Multivariate Fuzzy Decision Tree and User Adaptation

Hand Gesture Recognition Using Multivariate Fuzzy Decision Tree and User Adaptation

Moon-Jin Jeon, Sang Wan Lee, Zeungnam Bien
Copyright: © 2011 |Volume: 1 |Issue: 3 |Pages: 17
ISSN: 2156-177X|EISSN: 2156-1761|EISBN13: 9781613507070|DOI: 10.4018/ijfsa.2011070102
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MLA

Jeon, Moon-Jin, et al. "Hand Gesture Recognition Using Multivariate Fuzzy Decision Tree and User Adaptation." IJFSA vol.1, no.3 2011: pp.15-31. http://doi.org/10.4018/ijfsa.2011070102

APA

Jeon, M., Lee, S. W., & Bien, Z. (2011). Hand Gesture Recognition Using Multivariate Fuzzy Decision Tree and User Adaptation. International Journal of Fuzzy System Applications (IJFSA), 1(3), 15-31. http://doi.org/10.4018/ijfsa.2011070102

Chicago

Jeon, Moon-Jin, Sang Wan Lee, and Zeungnam Bien. "Hand Gesture Recognition Using Multivariate Fuzzy Decision Tree and User Adaptation," International Journal of Fuzzy System Applications (IJFSA) 1, no.3: 15-31. http://doi.org/10.4018/ijfsa.2011070102

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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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