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 |Pages: 17
DOI: 10.4018/ijfsa.2011070102
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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.
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Vision-Based Hand Gesture Recognition For The Soft Remote Control System

Vision-based hand gesture recognition for the soft remote control system (Do et al., 2005) is carried out in the following steps:

  • 1.

    Face and hand “region of interests” (ROI) are extracted from camera images.

  • 2.

    A trajectory of the hand position relative to the face position is calculated.

  • 3.

    A start position and an end position of the trajectory are segmented.

  • 4.

    Segmented trajectories are classified.

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