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- W3217005718 abstract "Due to the influence of face occlusion, side face and other factors, the recognition accuracy still needs to be improved in facial expression recognition(FER). This paper proposes a deep residual network incorporating an attention mechanism and deformable convolution to improve this phenomenon. The improved network is an attention mechanism embedded in the residual blocks with position information and a deformable convolution is added at the input to enhance the feature extraction capability for different locations of the image. This paper has been proved on the RAF-DB and FERPlus datasets, and the experimental results show that the accuracy of the improved network is 1.76% higher than the original network on the RAF-DB dataset and 2.76% higher than the original network on the FERPlus dataset." @default.
- W3217005718 created "2021-12-06" @default.
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- W3217005718 date "2021-09-24" @default.
- W3217005718 modified "2023-10-16" @default.
- W3217005718 title "ResNet with Attention Mechanism and Deformable Convolution for Facial Expression Recognition" @default.
- W3217005718 cites W3082185616 @default.
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- W3217005718 doi "https://doi.org/10.1109/icicsp54369.2021.9611962" @default.
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