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Depth-based hand gesture recognition

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Abstract

In this article, a dynamic gesture recognition system with the depth information is proposed. The proposed system consists of three main components: preprocessing, static posture recognition and dynamic gesture recognition. In the first component, the background subtraction is used to exclude invalid gestures that is not generated by the main user, and then to detect and track the hand. Second, the region of hand is extracted using an adaptive square. Once the region of hand is obtained, the features of hand are extracted and the static hand posture are classified using the support vector machine (SVM). Finally, nine commonly used dynamic hand gestures can be detected using different methods. In the experiments, the static hand posture classification was evaluated in different postures and the performance of dynamic gesture recognition is verified by two different persons at 4 different position with 2 different depths. The experiment results show that the proposed system can accurately detect the dynamic hand gestures with an average recognition rate of 87.6 %, which is good for controlling the embedded systems, such as home appliances.

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Authors and Affiliations

  1. Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, Taiwan, Republic of China

    Chih-Hung Wu, Wei-Lun Chen & Chang Hong Lin

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  1. Chih-Hung Wu
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  2. Wei-Lun Chen
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  3. Chang Hong Lin
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Correspondence to Chang Hong Lin.

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Wu, CH., Chen, WL. & Lin, C.H. Depth-based hand gesture recognition. Multimed Tools Appl 75, 7065–7086 (2016). https://doi.org/10.1007/s11042-015-2632-3

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  • DOI: https://doi.org/10.1007/s11042-015-2632-3

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