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Chinese Sign Language Identification via Wavelet Entropy and Support Vector Machine

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Book cover Advanced Data Mining and Applications (ADMA 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11888))

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Abstract

Sign language recognition is significant for smoothing barrier of communication between hearing-impaired people and health people. This paper proposed a novel Chinese sign language identification approach, in which wavelet entropy was adopted for feature reduction and support vector machine was employed for classification. The experiment was implemented on 10-fold cross validation. Our method (WE+SVM) yielded overall accuracy of 85.69 ± 0.59%. The results indicated this method was effective and superior to three state-of-the-art approaches.

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Acknowledgement

This work was supported from Jiangsu Overseas Visiting Scholar Program for University Prominent Young & Middle-aged Teachers and Presidents of China.

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Authors and Affiliations

  1. Nanjing Normal University of Special Education, Nanjing, 210038, China

    Xianwei Jiang & Zhaosong Zhu

  2. Department of Informatics, University of Leicester, Leicester, LE1 7RH, UK

    Xianwei Jiang

Authors
  1. Xianwei Jiang
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  2. Zhaosong Zhu
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Corresponding author

Correspondence to Xianwei Jiang .

Editor information

Editors and Affiliations

  1. Deakin University, Burwood, VIC, Australia

    Jianxin Li

  2. The University of Queensland, St. Lucia, QLD, Australia

    Sen Wang

  3. Flinders University, Bedford Park, SA, Australia

    Shaowen Qin

  4. Dalian Neusoft University of Information, Dalian, China

    Xue Li

  5. Beijing Institute of Technology, Beijing, China

    Shuliang Wang

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Jiang, X., Zhu, Z. (2019). Chinese Sign Language Identification via Wavelet Entropy and Support Vector Machine. In: Li, J., Wang, S., Qin, S., Li, X., Wang, S. (eds) Advanced Data Mining and Applications. ADMA 2019. Lecture Notes in Computer Science(), vol 11888. Springer, Cham. https://doi.org/10.1007/978-3-030-35231-8_53

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  • DOI: https://doi.org/10.1007/978-3-030-35231-8_53

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-35230-1

  • Online ISBN: 978-3-030-35231-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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