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Hand Gesture Recognition Using Multivariate Fuzzy Decision Tree and User Adaptation

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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)

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.

Suggested Citation

  • Moon-Jin Jeon & Sang Wan Lee & Zeungnam Bien, 2011. "Hand Gesture Recognition Using Multivariate Fuzzy Decision Tree and User Adaptation," International Journal of Fuzzy System Applications (IJFSA), IGI Global, vol. 1(3), pages 15-31, July.
    • Handle: RePEc:igg:jfsa00:v:1:y:2011:i:3:p:15-31
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    Cited by:

    1. Toly Chen & Yi-Chi Wang & Zhirong Lin, 2017. "Predictive distant operation and virtual control of computer numerical control machines," Journal of Intelligent Manufacturing, Springer, vol. 28(5), pages 1061-1077, June.

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