Facial expression recognition based on bidirectional gated recurrent units within deep residual network
International Journal of Intelligent Computing and Cybernetics
ISSN: 1756-378X
Article publication date: 6 November 2020
Issue publication date: 13 November 2020
Abstract
Purpose
recent years, facial expression recognition has been widely used in human machine interaction, clinical medicine and safe driving. However, there is a limitation that conventional recurrent neural networks can only learn the time-series characteristics of expressions based on one-way propagation information.
Design/methodology/approach
To solve such limitation, this paper proposes a novel model based on bidirectional gated recurrent unit networks (Bi-GRUs) with two-way propagations, and the theory of identity mapping residuals is adopted to effectively prevent the problem of gradient disappearance caused by the depth of the introduced network. Since the Inception-V3 network model for spatial feature extraction has too many parameters, it is prone to overfitting during training. This paper proposes a novel facial expression recognition model to add two reduction modules to reduce parameters, so as to obtain an Inception-W network with better generalization.
Findings
Finally, the proposed model is pretrained to determine the best settings and selections. Then, the pretrained model is experimented on two facial expression data sets of CK+ and Oulu- CASIA, and the recognition performance and efficiency are compared with the existing methods. The highest recognition rate is 99.6%, which shows that the method has good recognition accuracy in a certain range.
Originality/value
By using the proposed model for the applications of facial expression, the high recognition accuracy and robust recognition results with lower time consumption will help to build more sophisticated applications in real world.
Keywords
Acknowledgements
The authors have declared that there is no conflict of interest in the research. This paper is supported by a fund: science and technology research project of education department of Jiangxi province in 2019. (No GJJ191573.).
Citation
Shen, W. and Li, X. (2020), "Facial expression recognition based on bidirectional gated recurrent units within deep residual network", International Journal of Intelligent Computing and Cybernetics, Vol. 13 No. 4, pp. 527-543. https://doi.org/10.1108/IJICC-07-2020-0088
Publisher
:Emerald Publishing Limited
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