The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Facial Expression Recognition in the Wild via Deep Attentive Center Loss
Abstract
Learning discriminative features for Facial Expression Recognition (FER) in the wild using Convolutional Neural Networks (CNNs) is a non-trivial task due to the significant intra-class variations and inter-class similarities. Deep Metric Learning (DML) approaches such as center loss and its variants jointly optimized with softmax loss have been adopted in many FER methods to enhance the discriminative power of learned features in the embedding space. However, equally supervising all features with the metric learning method might include irrelevant features and ultimately degrade the generalization ability of the learning algorithm. We propose a Deep Attentive Center Loss (DACL) method to adaptively select a subset of significant feature elements for enhanced discrimination. The proposed DACL integrates an attention mechanism to estimate attention weights correlated with feature importance using the intermediate spatial feature maps in CNN as context. The estimated weights accommodate the sparse formulation of center loss to selectively achieve intra-class compactness and inter-class separation for the relevant information in the embedding space. An extensive study on two widely used wild FER datasets demonstrates the superiority of the proposed DACL method compared to state-of-the-art methods.
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Cites
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Suppressing Uncertainties for Large-Scale Facial Expression Recognition
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Cited by
Savchenko, A. V., Savchenko, L. V., & Makarov, I. (2022) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2022.3188390 · Cited by 346
Face2Exp: Combating Data Biases for Facial Expression Recognition
Zeng, D., Lin, Z., Yan, X., Liu, Y., Wang, F., & Tang, B. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01965 · Cited by 141
Adaptive Multilayer Perceptual Attention Network for Facial Expression Recognition
Liu, H., Cai, H., Lin, Q., Li, X., & Xiao, H. (2022) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2022.3165321 · Cited by 129
Detection and Localization of Facial Expression Manipulations
Mazaheri, G., & Roy-Chowdhury, A. K. (2022) · 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv51458.2022.00283 · Cited by 44
Ben Ahmed, K., Hall, L. O., Goldgof, D. B., & Fogarty, R. (2022) · IEEE Access · doi:10.1109/access.2022.3193700 · Cited by 12
Metadata
- Authors
- Farzaneh, A. H., & Qi, X.
- Venue
- 2021 IEEE Winter Conference on Applications of Computer Vision (WACV)
- Publisher
- IEEE
- Published
- 2021-01
- Pages
- 2401-2410
- ISSN
- 2472-6737 (ISSN-L)
- OpenAlex
- W3118530108
- Cited by
- 326
- References
- 10
- FWCI
- 21.57
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Farzaneh, A. H., & Qi, X. (2021). Facial Expression Recognition in the Wild via Deep Attentive Center Loss. 2021 IEEE Winter Conference on Applications of Computer Vision (WACV). https://doi.org/10.1109/wacv48630.2021.00245
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