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International Conference on Smart Multimedia

ICSM 2018: Smart Multimedia pp 219–231Cite as

A Survey on Vision-Based Hand Gesture Recognition

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11010))

Abstract

Hand gesture recognition is regarded as an important part of artificial intelligence. A great effort was put into human-computer interaction so that hand gesture recognition is gradually becoming a developed technology. In light of the utilization of mouse and keyboard, the increasing needs of human-computer interaction cannot be met; hindrance turns out to be increasingly genuine. In this paper, we reviewed previous investigations of vision-based gesture recognition and summarized their findings. This paper compares the most common human-computer interaction products in recent years, which can be used to capture gesture data. Then we started with the classification of gestures and summarized the research of visual gesture recognition based on static and dynamic gestures. The gesture representations we summarized includes appearance-based and 3D model-based methods. We also introduced the applications of the two kinds of hand gestures recognition in the papers of recent years. A possible classification methods was put forward to improve the performance of gesture recognition. The goal of this paper is to summarize the current technology and research results and compare the differences and the advantage of different hand gesture recognition methods, which will contribute to the following research.

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Acknowledgement

This work was supported by grants from the Fundamental Research Funds for the Key Research Programm of Chongqing Science & Technology Commission (grant no. cstc2017rgzn-zdyf0064), the Chongqing Provincial Human Resource and Social Security Department (grant no. cx2017092), the Central Universities in China (grant nos. 2018CDXYRJ0030, CQU0225001104447), Science and Technology Innovation Project of Foshan City, China (grant no. 2015IT100095), the Fundamental Research Funds for the Central Universities (grant no. lzujbkey-2016-br03), CERNET Innovation Project (grant no. NGII20150603) and Science and Technology Planning Project of Guangdong Province, China (grant no. 2016B010108002).

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Authors and Affiliations

  1. School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, China

    Taiqian Wang, Yande Li & Caihong Li

  2. School of Software Engineering, Chongqing University, Chongqing, 401331, China

    Junfeng Hu, Aamir Khan, Li Liu & Mengyuan Ran

  3. School of Software Engineering, Beijing Institute of Technology, Beijing, 100080, China

    Ammarah Hashmi

Authors
  1. Taiqian Wang
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  2. Yande Li
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  3. Junfeng Hu
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  4. Aamir Khan
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  5. Li Liu
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  6. Caihong Li
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  7. Ammarah Hashmi
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  8. Mengyuan Ran
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Corresponding author

Correspondence to Li Liu .

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Editors and Affiliations

  1. University of Alberta, Edmonton, AB, Canada

    Anup Basu

  2. Dipartimento di Ingegneria, Università degli Studi di Firenze, Florence, Italy

    Stefano Berretti

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Wang, T. et al. (2018). A Survey on Vision-Based Hand Gesture Recognition. In: Basu, A., Berretti, S. (eds) Smart Multimedia. ICSM 2018. Lecture Notes in Computer Science(), vol 11010. Springer, Cham. https://doi.org/10.1007/978-3-030-04375-9_19

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  • DOI: https://doi.org/10.1007/978-3-030-04375-9_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-04374-2

  • Online ISBN: 978-3-030-04375-9

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