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Gesture recognition of graph convolutional neural network based on spatial domain

  • S.I.: Machine Learning and Big Data Analytics for IoT Security and Privacy (SPIOT 2021)
  • Published:
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Abstract

With the iterative update of computer technology, the penetration of computer and other Internet technologies in human–computer interaction systems has become more and more extensive, and the human–computer interaction methods have quietly undergone huge changes. Gesture recognition has gradually become a hot spot in the field of human–computer interaction now, which has a wide range of application prospects and research value. The color segmentation experiment shows that the skin color of the gesture in the YCrCg space has better clustering properties than in the YCrCb space. In the preprocessing of gesture images, an improved Otsu method is proposed to improve the real-time performance to realize the threshold segmentation of the human hand; then the morphological processing is carried out, and the median filter method is used to achieve image denoising to improve image quality. A gesture recognition algorithm is designed: First, use Graph-SAGE to recognize the graph-structured data of the gesture, and then use the Adaboost algorithm to combine the two strong classifiers of the random forest and the support vector machine into a cascade classifier through the cascade structure. The output information of Graph-SAGE is classified and the meaning of the gesture is analyzed. On the test set, the average detection accuracy of the algorithm is 91.70%, the recall rate is 94.23%, and the average detection time per frame is 330 ms.

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Acknowledgments

The study was supported by “Tianjin science and technology project (Grant No. 21YDTPJC00050)” and “science and technology on electro-optical information security control laboratury project (Grant No. 2021JCJQLB055008)”.

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Authors and Affiliations

  1. School of Electronic and Information Engineering, Hebei University of Technology, Tianjin, 300130, China

    Hong Chen & Hongdong Zhao

  2. School of Mathematics and Information Technology, Hebei Normal University of Science and Technology, Qinhuangdao, 066004, Hebei, China

    Hong Chen, Shuai Zhang & Zhanghong Yu

  3. Department of Information Engineering, Qinhuangdao Institute of Technology, Qinhuangdao, 066100, Hebei, China

    Baoqiang Qi

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  1. Hong Chen
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Correspondence to Hongdong Zhao.

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Chen, H., Zhao, H., Qi, B. et al. Gesture recognition of graph convolutional neural network based on spatial domain. Neural Comput & Applic 35, 2157–2167 (2023). https://doi.org/10.1007/s00521-022-07040-8

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  • DOI: https://doi.org/10.1007/s00521-022-07040-8

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