Feature Decomposition and Reconstruction Learning for Effective Facial Expression Recognition

@article{Ruan2021FeatureDA,
  title={Feature Decomposition and Reconstruction Learning for Effective Facial Expression Recognition},
  author={Delian Ruan and Yan Yan and Shenqi Lai and Zhenhua Chai and Chunhua Shen and Hanzi Wang},
  journal={2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2021},
  pages={7656-7665}
}
  • D. Ruan, Yan Yan, +3 authors Hanzi Wang
  • Published 12 April 2021
  • Computer Science
  • 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
In this paper, we propose a novel Feature Decomposition and Reconstruction Learning (FDRL) method for effective facial expression recognition. We view the expression information as the combination of the shared information (expression similarities) across different expressions and the unique information (expression-specific variations) for each expression. More specifically, FDRL mainly consists of two crucial networks: a Feature Decomposition Network (FDN) and a Feature Reconstruction Network… Expand

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