Hand gesture recognition using multivariate fuzzy decision tree and user adaptation

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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. © 2011, IGI Global.
Publisher
IGI Publishing
Issue Date
2011-07
Language
English
Citation

International Journal of Fuzzy System Applications, v.1, no.3, pp.15 - 31

ISSN
2156-177X
DOI
10.4018/ijfsa.2011070102
URI
http://hdl.handle.net/10203/207232
Appears in Collection
BiS-Journal Papers(저널논문)EE-Journal Papers(저널논문)
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