Facial Expression Recognition Method Based on Residual Masking Reconstruction Network


Jianing Shen, Hongmei Li, Journal of Information Processing Systems Vol. 19, No. 3, pp. 323-333, Jun. 2023  

https://doi.org/10.3745/JIPS.02.0198
Keywords: Data Dimension, Feature Dimension, Image Analysis, Loss Function, Residual Masking ReconstructionNetwork
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Abstract

Facial expression recognition can aid in the development of fatigue driving detection, teaching quality evaluation, and other fields. In this study, a facial expression recognition method was proposed with a residual masking reconstruction network as its backbone to achieve more efficient expression recognition and classification. The residual layer was used to acquire and capture the information features of the input image, and the masking layer was used for the weight coefficients corresponding to different information features to achieve accurate and effective image analysis for images of different sizes. To further improve the performance of expression analysis, the loss function of the model is optimized from two aspects, feature dimension and data dimension, to enhance the accurate mapping relationship between facial features and emotional labels. The simulation results show that the ROC of the proposed method was maintained above 0.9995, which can accurately distinguish different expressions. The precision was 75.98%, indicating excellent performance of the facial expression recognition model.


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Cite this article
[APA Style]
Shen, J. & Li, H. (2023). Facial Expression Recognition Method Based on Residual Masking Reconstruction Network. Journal of Information Processing Systems, 19(3), 323-333. DOI: 10.3745/JIPS.02.0198.

[IEEE Style]
J. Shen and H. Li, "Facial Expression Recognition Method Based on Residual Masking Reconstruction Network," Journal of Information Processing Systems, vol. 19, no. 3, pp. 323-333, 2023. DOI: 10.3745/JIPS.02.0198.

[ACM Style]
Jianing Shen and Hongmei Li. 2023. Facial Expression Recognition Method Based on Residual Masking Reconstruction Network. Journal of Information Processing Systems, 19, 3, (2023), 323-333. DOI: 10.3745/JIPS.02.0198.