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• W4313132476 endingPage "2047" @default.
• W4313132476 startingPage "2033" @default.
• W4313132476 abstract "Facial Expression Recognition (FER) has recently emerged as a crucial area in Human-Computer Interaction (HCI) system for understanding the user’s inner state and intention. However, feature- and label-noise constitute the major challenge for FER in the wild due to the ambiguity of facial expressions worsened by low-quality images. To deal with this problem, in this paper, we propose a simple but effective Facial Expression Noise-tolerant Network (FENN) which explores the inter-class correlations for mitigating ambiguity that usually happens between morphologically similar classes. Specifically, FENN leverages a multivariate normal distribution to model such correlations at the final hidden layer of the neural network to suppress the heteroscedastic uncertainty caused by inter-class label noise. Furthermore, the discriminative ability of deep features is weakened by the subtle differences between expressions and the presence of feature noise. FENN utilizes a feature-noise mitigation module to extract compact intra-class feature representations under feature noise while preserving the intrinsic inter-class relationships. We conduct extensive experiments to evaluate the effectiveness of FENN on both original annotated images and synthetic noisy annotated images from RAF-DB, AffectNet, and FERPlus in-the-wild facial expression datasets. The results show that FENN significantly outperforms state-of-the-art FER methods." @default.
• W4313132476 created "2023-01-06" @default.
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• W4313132476 date "2023-05-01" @default.
• W4313132476 modified "2023-09-26" @default.
• W4313132476 title "Toward Facial Expression Recognition in the Wild via Noise-Tolerant Network" @default.
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• W4313132476 doi "https://doi.org/10.1109/tcsvt.2022.3220669" @default.
• W4313132476 hasPublicationYear "2023" @default.
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