A novel facial expression recognition framework using deep learning based dynamic cross-domain dual attention network
Variations in domain targets have recently posed significant challenges for facial expression recognition tasks, primarily due to domain shifts. Current methods focus largely on global feature adoption to achieve domain-invariant learning; however, transferring local features across diverse domains...
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2025-05-01
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| Series: | PeerJ Computer Science |
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| Online Access: | https://peerj.com/articles/cs-2866.pdf |
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| author | Ahmed Omar Alzahrani Ahmed Mohammed Alghamdi M. Usman Ashraf Iqra Ilyas Nadeem Sarwar Abdulrahman Alzahrani Alaa Abdul Salam Alarood |
| author_facet | Ahmed Omar Alzahrani Ahmed Mohammed Alghamdi M. Usman Ashraf Iqra Ilyas Nadeem Sarwar Abdulrahman Alzahrani Alaa Abdul Salam Alarood |
| author_sort | Ahmed Omar Alzahrani |
| collection | DOAJ |
| description | Variations in domain targets have recently posed significant challenges for facial expression recognition tasks, primarily due to domain shifts. Current methods focus largely on global feature adoption to achieve domain-invariant learning; however, transferring local features across diverse domains remains an ongoing challenge. Additionally, during training on target datasets, these methods often suffer from reduced feature representation in the target domain due to insufficient discriminative supervision. To tackle these challenges, we propose a dynamic cross-domain dual attention network for facial expression recognition. Our model is specifically designed to learn domain-invariant features through separate modules for global and local adversarial learning. We also introduce a semantic-aware module to generate pseudo-labels, which computes semantic labels from both global and local features. We assess our model’s effectiveness through extensive experiments on the Real-world Affective Faces Database (RAF-DB), FER-PLUS, AffectNet, Expression in the Wild (ExpW), SFEW 2.0, and Japanese Female Facial Expression (JAFFE) datasets. The results demonstrate that our scheme outperforms the existing state-of-the-art methods by attaining recognition accuracies 93.18, 92.35, 82.13, 78.37, 72.47, 70.68 respectively. |
| format | Article |
| id | doaj-art-e2f71710501f4a6b817edd472cd75893 |
| institution | DOAJ |
| issn | 2376-5992 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj-art-e2f71710501f4a6b817edd472cd758932025-08-20T03:09:32ZengPeerJ Inc.PeerJ Computer Science2376-59922025-05-0111e286610.7717/peerj-cs.2866A novel facial expression recognition framework using deep learning based dynamic cross-domain dual attention networkAhmed Omar Alzahrani0Ahmed Mohammed Alghamdi1M. Usman Ashraf2Iqra Ilyas3Nadeem Sarwar4Abdulrahman Alzahrani5Alaa Abdul Salam Alarood6Department of Information Systems and Technology, College of Computer Science and Engineering, University of Jeddah, Jeddah, Makkah, Saudi ArabiaDepartment of Software Engineering, College of Computer Science and Engineering, University of Jeddah, Jeddah, Makkah, Saudi ArabiaDepartment of Computer Science, Government College Women University Sialkot, Sialkot, Punjab, PakistanDepartment of Computer Science, Government College Women University Sialkot, Sialkot, Punjab, PakistanDepartment of Computer Science, Bahria University, Lahore, Punjab, PakistanDepartment of Information Systems and Technology, College of Computer Science and Engineering, University of Jeddah, Jeddah, Makkah, Saudi ArabiaDepartment of Information Systems and Technology, College of Computer Science and Engineering, University of Jeddah, Jeddah, Makkah, Saudi ArabiaVariations in domain targets have recently posed significant challenges for facial expression recognition tasks, primarily due to domain shifts. Current methods focus largely on global feature adoption to achieve domain-invariant learning; however, transferring local features across diverse domains remains an ongoing challenge. Additionally, during training on target datasets, these methods often suffer from reduced feature representation in the target domain due to insufficient discriminative supervision. To tackle these challenges, we propose a dynamic cross-domain dual attention network for facial expression recognition. Our model is specifically designed to learn domain-invariant features through separate modules for global and local adversarial learning. We also introduce a semantic-aware module to generate pseudo-labels, which computes semantic labels from both global and local features. We assess our model’s effectiveness through extensive experiments on the Real-world Affective Faces Database (RAF-DB), FER-PLUS, AffectNet, Expression in the Wild (ExpW), SFEW 2.0, and Japanese Female Facial Expression (JAFFE) datasets. The results demonstrate that our scheme outperforms the existing state-of-the-art methods by attaining recognition accuracies 93.18, 92.35, 82.13, 78.37, 72.47, 70.68 respectively.https://peerj.com/articles/cs-2866.pdfArtificial intelligenceFacial expression recognitionDeep learningCross-domains |
| spellingShingle | Ahmed Omar Alzahrani Ahmed Mohammed Alghamdi M. Usman Ashraf Iqra Ilyas Nadeem Sarwar Abdulrahman Alzahrani Alaa Abdul Salam Alarood A novel facial expression recognition framework using deep learning based dynamic cross-domain dual attention network PeerJ Computer Science Artificial intelligence Facial expression recognition Deep learning Cross-domains |
| title | A novel facial expression recognition framework using deep learning based dynamic cross-domain dual attention network |
| title_full | A novel facial expression recognition framework using deep learning based dynamic cross-domain dual attention network |
| title_fullStr | A novel facial expression recognition framework using deep learning based dynamic cross-domain dual attention network |
| title_full_unstemmed | A novel facial expression recognition framework using deep learning based dynamic cross-domain dual attention network |
| title_short | A novel facial expression recognition framework using deep learning based dynamic cross-domain dual attention network |
| title_sort | novel facial expression recognition framework using deep learning based dynamic cross domain dual attention network |
| topic | Artificial intelligence Facial expression recognition Deep learning Cross-domains |
| url | https://peerj.com/articles/cs-2866.pdf |
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