Hand gesture recognition based on convolution neural network

Gongfa Li, Heng Tang, Ying Sun, Jianyi Kong, Guozhang Jiang, Du Jiang, Bo Tao, Shuang Xu, Honghai Liu

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
JournalCluster Computing
Early online date29 Dec 2017
Publication statusEarly online - 29 Dec 2017


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