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research-article

Region Attention Networks for Pose and Occlusion Robust Facial Expression Recognition

Authors:

Xiaojiang Peng,

Yu QiaoAuthors Info & Claims

IEEE Transactions on Image Processing, Volume 29

Pages 4057 - 4069

https://doi.org/10.1109/TIP.2019.2956143

Published: 01 January 2020 Publication History

Abstract

Occlusion and pose variations, which can change facial appearance significantly, are two major obstacles for automatic Facial Expression Recognition (FER). Though automatic FER has made substantial progresses in the past few decades, occlusion-robust and pose-invariant issues of FER have received relatively less attention, especially in real-world scenarios. This paper addresses the real-world pose and occlusion robust FER problem in the following aspects. First, to stimulate the research of FER under real-world occlusions and variant poses, we annotate several in-the-wild FER datasets with pose and occlusion attributes for the community. Second, we propose a novel Region Attention Network (RAN), to adaptively capture the importance of facial regions for occlusion and pose variant FER. The RAN aggregates and embeds varied number of region features produced by a backbone convolutional neural network into a compact fixed-length representation. Last, inspired by the fact that facial expressions are mainly defined by facial action units, we propose a region biased loss to encourage high attention weights for the most important regions. We validate our RAN and region biased loss on both our built test datasets and four popular datasets: FERPlus, AffectNet, RAF-DB, and SFEW. Extensive experiments show that our RAN and region biased loss largely improve the performance of FER with occlusion and variant pose. Our method also achieves state-of-the-art results on FERPlus, AffectNet, RAF-DB, and SFEW. Code and the collected test data will be publicly available.

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Cited By

Zhang XZhou JQi G(2025)Multimodal temporal context network for tracking dynamic changes in emotionThe Journal of Supercomputing10.1007/s11227-024-06484-081:1Online publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1007/s11227-024-06484-0
Zhang SXiao Z(2024)Facial Expression Recognition Using a Semantic-Based Bottleneck Attention ModuleInternational Journal on Semantic Web & Information Systems10.4018/IJSWIS.35241820:1(1-25)Online publication date: 17-Sep-2024
https://dl.acm.org/doi/10.4018/IJSWIS.352418
Yang WChen HGuo JPeng HLiu Z(2024)Refining Facial Expression Recognition with Bilinear ResSpikeNet (BRS-Net): ADeep Learning ApproachProceedings of the 2024 2nd Asia Conference on Computer Vision, Image Processing and Pattern Recognition10.1145/3663976.3664027(1-6)Online publication date: 26-Apr-2024
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Index Terms

Region Attention Networks for Pose and Occlusion Robust Facial Expression Recognition
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Object recognition
      2. Computer vision tasks
  2. Machine learning
    1. Machine learning approaches
      1. Neural networks

Index terms have been assigned to the content through auto-classification.

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cover image IEEE Transactions on Image Processing

IEEE Transactions on Image Processing Volume 29, Issue

2020

3918 pages

ISSN:1057-7149

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1057-7149 © 2020 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See https://www.ieee.org/publications/rights/index.html for more information.

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Published: 01 January 2020

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Cited By

Zhang XZhou JQi G(2025)Multimodal temporal context network for tracking dynamic changes in emotionThe Journal of Supercomputing10.1007/s11227-024-06484-081:1Online publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1007/s11227-024-06484-0
Zhang SXiao Z(2024)Facial Expression Recognition Using a Semantic-Based Bottleneck Attention ModuleInternational Journal on Semantic Web & Information Systems10.4018/IJSWIS.35241820:1(1-25)Online publication date: 17-Sep-2024
https://dl.acm.org/doi/10.4018/IJSWIS.352418
Yang WChen HGuo JPeng HLiu Z(2024)Refining Facial Expression Recognition with Bilinear ResSpikeNet (BRS-Net): ADeep Learning ApproachProceedings of the 2024 2nd Asia Conference on Computer Vision, Image Processing and Pattern Recognition10.1145/3663976.3664027(1-6)Online publication date: 26-Apr-2024
https://dl.acm.org/doi/10.1145/3663976.3664027
Cheheb I(2024)An Investigation into the Impact of Occlusion on Facial Emotion Recognition in the WildProceedings of the 17th International Conference on PErvasive Technologies Related to Assistive Environments10.1145/3652037.3663932(365-368)Online publication date: 26-Jun-2024
https://dl.acm.org/doi/10.1145/3652037.3663932
Wang SZhong LFu YChen LRen JZhang Y(2024)UFaceProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36435468:1(1-27)Online publication date: 6-Mar-2024
https://dl.acm.org/doi/10.1145/3643546
Lv YHuang GYan YXue JChen SWang H(2024)Visual-Textual Attribute Learning for Class-Incremental Facial Expression RecognitionIEEE Transactions on Multimedia10.1109/TMM.2024.337457326(8038-8051)Online publication date: 12-Mar-2024
https://dl.acm.org/doi/10.1109/TMM.2024.3374573
Liu YZhang XKauttonen JZhao G(2024)Uncertain Facial Expression Recognition via Multi-Task Assisted CorrectionIEEE Transactions on Multimedia10.1109/TMM.2023.330120926(2531-2543)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TMM.2023.3301209
Xie WPeng ZShen LLu WZhang YSong S(2024)Cross-Layer Contrastive Learning of Latent Semantics for Facial Expression RecognitionIEEE Transactions on Image Processing10.1109/TIP.2024.337845933(2514-2529)Online publication date: 27-Mar-2024
https://dl.acm.org/doi/10.1109/TIP.2024.3378459
Lv YYan YXue JChen SWang H(2024)Relationship-Guided Knowledge Transfer for Class-Incremental Facial Expression RecognitionIEEE Transactions on Image Processing10.1109/TIP.2024.337411633(2293-2304)Online publication date: 1-Jan-2024
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Zhou HHuang SZhang FXu C(2024)CEPrompt: Cross-Modal Emotion-Aware Prompting for Facial Expression RecognitionIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2024.342477734:11_Part_2(11886-11899)Online publication date: 8-Jul-2024
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