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Hand gesture recognition based on convolution neural network

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Abstract

Due to the complexity issue of the hand gesture recognition feature extraction, for example the variation of the light and background. In this paper, the convolution neural network is applied to the recognition of gestures, and the characteristics of convolution neural network are used to avoid the feature extraction process, reduce the number of parameters needs to be trained, and finally achieve the purpose of unsupervised learning. Error back propagation algorithm, is loaded into the convolution neural network algorithm, modify the threshold and weights of neural network to reduce the error of the model. In the classifier, the support vector machine that is added to optimize the classification function of the convolution neural network to improve the validity and robustness of the whole model.

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Acknowledgements

This work was supported by Grants of National Natural Science Foundation of China (Grant Nos. 51575407, 51575338, 51575412, 61273106).

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Authors and Affiliations

  1. Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan, 430081, China

    Gongfa Li, Heng Tang, Ying Sun, Jianyi Kong, Guozhang Jiang, Bo Tao & Shuang Xu

  2. Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan, 430081, China

    Gongfa Li, Ying Sun, Jianyi Kong, Guozhang Jiang, Du Jiang, Bo Tao & Shuang Xu

  3. School of Computing, University of Portsmouth, Portsmouth, PO1 3HE, UK

    Honghai Liu

Authors
  1. Gongfa Li
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  2. Heng Tang
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  3. Ying Sun
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  4. Jianyi Kong
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  5. Guozhang Jiang
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  6. Du Jiang
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  9. Honghai Liu
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Correspondence to Heng Tang.

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Li, G., Tang, H., Sun, Y. et al. Hand gesture recognition based on convolution neural network. Cluster Comput 22 (Suppl 2), 2719–2729 (2019). https://doi.org/10.1007/s10586-017-1435-x

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