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Similar hand gesture recognition by automatically extracting distinctive features

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  • Robot and Applications
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Abstract

With the flourish development of computer vision technology, hand gesture recognition plays a more and more vital role in human-computer interaction for its convenient and nonverbal communication. However, confusion caused by similar gestures brings inherent errors when considering enough meaningful gestures in the database. In this paper, an automatic feature extraction for similar gesture recognition is proposed with respect to confusion arising in similar gestures. Except the orientation feature, four additional innovative features are extracted to distinguish all the similar gestures remarkably in the experimental database containing 10 numbers and 26 letters. Compared with the conventional method that a couple of similar gestures are extracted as a specific feature, the proposed method distinguishes similar gestures with automatic distinctive feature extraction. Experimental results show high recognition rate and versatility of the proposed method.

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Author information

Authors and Affiliations

  1. Guangdong Provincial Key Laboratory of Robotics and Intelligent System, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

    Zeyu Ding, Yanmei Chen & Xinyu Wu

  2. Department of Industry Technology Education, National Kaohsiung Normal University, Taiwan, China

    Yen-Lun Chen

  3. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Hong Kong, China

    Xinyu Wu

Authors
  1. Zeyu Ding
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  2. Yanmei Chen
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  3. Yen-Lun Chen
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  4. Xinyu Wu
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Corresponding author

Correspondence to Yen-Lun Chen.

Additional information

Recommended by Associate Editor Huaping Liu under the direction of Editor Euntai Kim. The work described in this paper is partially supported by National Science Foundation of China (61403364, 61473277), Shenzhen Fundamental Research Program (JCYJ2014091003939022), Guandong public welfare research and capacity building project (2014A010103020), and Guangdong Innovative Research Team Program (201001D0104648280).

Zeyu Ding received his Master degree from University of Science and Technology of China in 2016, and Bachelor of Engineering degree in Electronic Information Engineering from Hefei university of technology in 2013. His research interests include gesture recognition and computer vision.

Yanmei Chen received her Master degree from Wuyi University in 2016, and Bachelor of Engineering degree in Information and Communication Engineering from Wuyi University in 2013. Her research interests include image processing and computer vision.

Yen-Lun Chen received her B.S. and M.S. degrees from Department of Electrical Engineering at National Taiwan University, and Ph.D. degree from Department of Electrical and Computer Engineering at the Ohio State University. Her research interests include machine learning, pattern recognition, computer vision, and multimedia signal processing.

Xinyu Wu is a Professor at Shenzhen Institutes of Advanced Technology, and an associate director of Center for Intelligent and Biomimetic Systems. He received his BE and ME degrees from Department of Automation, University of Science and Technology of China, in 2001 and 2004, respectively. His PhD degree was awarded at the Chinese University of Hong Kong in 2008. He has published over 80 papers and a monograph. His research interests include computer vision, robotics, and intelligent system.

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Ding, Z., Chen, Y., Chen, YL. et al. Similar hand gesture recognition by automatically extracting distinctive features. Int. J. Control Autom. Syst. 15, 1770–1778 (2017). https://doi.org/10.1007/s12555-015-0403-6

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  • DOI: https://doi.org/10.1007/s12555-015-0403-6

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