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HMM Static Hand Gesture Recognition Based on Combination of Shape Features and Wavelet Texture Features

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Abstract

Gesture recognition is one of the key technologies in the field of computer vision, and hand gesture recognition can be divided into static hand gesture recognition and the dynamic hand gesture recognition. This paper presents a new static gesture recognition algorithm based on hidden markov model. It uses two kinds of new shape features, the specific angle shape entropy feature and the upper side contour feature. They are firstly used for parameters training of hidden makov model, and then identify gesture categories hierarchically. In order to further improve the recognition effect for those small shape differences gesture, this paper adopts wavelet texture energy feature which can reflect the internal details of the gesture image, and makes the final correction estimation based on minimum total error probability. The experimental results show that the method has good recognition effects for gestures no matter the shape differences are big or not, and it has good real time performance as well.

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Acknowledgement

This work is supported by the Harbin Science and Technology Bureau outstanding subject leader fund project (2017RAXXJ055), Nature Science Foundation of Heilongjiang Province (F2018020).

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Authors and Affiliations

  1. School of Computer and Information Engineering, Harbin University of Commerce, Harbin, 150028, China

    Lizhi Zhang, Zhijie Zhao & Xiaowei Han

  2. School of Information and Communication Engineering, Harbin Engineering University, Harbin, 150001, China

    Yingrui Zhang

  3. Network and Educational Technology Center, Harbin University of Commerce, Harbin, 150028, China

    Lianding Niu

  4. Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing, Harbin University of Commerce, Harbin, 150028, China

    Lizhi Zhang, Zhijie Zhao & Xiaowei Han

Authors
  1. Lizhi Zhang
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  2. Yingrui Zhang
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  3. Lianding Niu
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  4. Zhijie Zhao
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  5. Xiaowei Han
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Corresponding author

Correspondence to Lizhi Zhang .

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Editors and Affiliations

  1. Harbin Institute of Technology, Harbin, China

    Min Jia

  2. Harbin Institute of Technology, Harbin, China

    Qing Guo

  3. Harbin Institute of Technology, Harbin, China

    Weixiao Meng

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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Zhang, L., Zhang, Y., Niu, L., Zhao, Z., Han, X. (2019). HMM Static Hand Gesture Recognition Based on Combination of Shape Features and Wavelet Texture Features. In: Jia, M., Guo, Q., Meng, W. (eds) Wireless and Satellite Systems. WiSATS 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 281. Springer, Cham. https://doi.org/10.1007/978-3-030-19156-6_18

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  • DOI: https://doi.org/10.1007/978-3-030-19156-6_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-19155-9

  • Online ISBN: 978-3-030-19156-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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