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Discriminative attention-augmented feature learning for facial expression recognition in the wild
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Zhou, Linyi, Fan, Xijian, Tjahjadi, Tardi and Das Choudhury, Sruti (2022) Discriminative attention-augmented feature learning for facial expression recognition in the wild. Neural Computing and Applications, 34 . pp. 925-936. doi:10.1007/s00521-021-06045-z ISSN 0941-0643.
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WRAP-Discriminative-attention-augmented-facial-expression-recognition-2021.pdf - Accepted Version - Requires a PDF viewer. Download (1228Kb) | Preview |
Official URL: http://dx.doi.org/10.1007/s00521-021-06045-z
Abstract
Facial expression recognition (FER) in-the-wild is challenging due to unconstraint settings such as varying head poses, illumination, and occlusions. In addition, the performance of a FER system significantly degrades due to large intra-class variation and inter-class similarity of facial expressions in real-world scenarios. To mitigate these problems, we propose a novel approach, Discriminative Attention-augmented Feature Learning Convolution Neural Network (DAF-CNN), which learns discriminative expression-related representations for FER. Firstly, we develop a 3D attention mechanism for feature refinement which selectively focuses on attentive channel entries and salient spatial regions of a convolution neural network feature map. Moreover, a deep metric loss termed Triplet-Center (TC) loss is incorporated to further enhance the discriminative power of the deeply-learned features with an expression-similarity constraint. It simultaneously minimizes intra-class distance and maximizes inter-class distance to learn both compact and separate features. Extensive experiments have been conducted on two representative facial expression datasets (FER-2013 and SFEW 2.0) to demonstrate that DAF-CNN effectively captures discriminative feature representations and achieves competitive or even superior FER performance compared to state-of-the-art FER methods.
Item Type: | Journal Article | ||||||||
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Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software Q Science > QP Physiology T Technology > TA Engineering (General). Civil engineering (General) |
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Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | ||||||||
Library of Congress Subject Headings (LCSH): | Facial expression, Human face recognition (Computer science), Emotion recognition, Pattern recognition systems, Neural networks (Computer science) | ||||||||
Journal or Publication Title: | Neural Computing and Applications | ||||||||
Publisher: | Springer | ||||||||
ISSN: | 0941-0643 | ||||||||
Official Date: | January 2022 | ||||||||
Dates: |
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Volume: | 34 | ||||||||
Page Range: | pp. 925-936 | ||||||||
DOI: | 10.1007/s00521-021-06045-z | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Reuse Statement (publisher, data, author rights): | This is a post-peer-review, pre-copyedit version of an article published in Neural Computing and Applications. The final authenticated version is available online at: http://dx.doi.org/10.1007/s00521-021-06045-z | ||||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||||
Date of first compliant deposit: | 18 May 2021 | ||||||||
Date of first compliant Open Access: | 29 April 2022 |
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