Electronic Science and Technology ›› 2020, Vol. 33 ›› Issue (9): 16-20.doi: 10.16180/j.cnki.issn1007-7820.2020.09.003

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Expression Recognition Method Based on Cascade Network Optimized by SWA

ZHANG Xiang,SHI Zhicai,CHEN Liang   

  1. School of Electronic and Electrical Engineering,Shanghai University of Engineering Science,Shanghai 201620,China
  • Received:2019-07-02 Online:2020-09-15 Published:2020-09-12
  • Supported by:
    National Natural Science Foundation of China(61802252)


In order to improve the detection accuracy of expression recognition technology, a face expression recognition method based on cascade network optimized by SWA is proposed. Compared with a single convolutional network, multi-network cascading reachs higher detection accuracy. With respect to the traditional training method SGD, SWA training method enhances the generalization ability of the sub-network in the cascade network, which further improves the overall performance of the model. By testing on the Fer2013 dataset, the experimental results shows that the detection accuracy of the network cascaded by the way of weighted summation based on SWA training method reachs 74.478%, which is 1.4% higher than the single network model trained by the traditional SGD method. In addition, the improved model proposed in the present study reachs a higher recognition accuracy than other typical methods.

Key words: expression recognition, convolutional neural network, stochastic weight averaging, stochastic gradient descent, Fer2013 dataset, network cascade

CLC Number: 

  • TP391