A Novel Transformer-Based Approach for Adult’s Facial Emotion Recognition
<p dir="ltr">Adult facial expression recognition (FER) is essential for human-computer interaction, mental health assessment, and social robotics applications because it improves user experiences and emotional well-being. This study presents a novel attention mechanism-based transfor...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , |
| منشور في: |
2025
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| الموضوعات: | |
| الوسوم: |
إضافة وسم
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| _version_ | 1864513534679842816 |
|---|---|
| author | Uzma Nawaz (21980708) |
| author2 | Zubair Saeed (19325647) Kamran Atif (22457845) |
| author2_role | author author |
| author_facet | Uzma Nawaz (21980708) Zubair Saeed (19325647) Kamran Atif (22457845) |
| author_role | author |
| dc.creator.none.fl_str_mv | Uzma Nawaz (21980708) Zubair Saeed (19325647) Kamran Atif (22457845) |
| dc.date.none.fl_str_mv | 2025-04-04T06:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2025.3555510 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/A_Novel_Transformer-Based_Approach_for_Adult_s_Facial_Emotion_Recognition/30393349 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Information and computing sciences Artificial intelligence Computer vision and multimedia computation Machine learning Facial emotion recognition transformers deep learning FER2013 CK+ AffectNet AFEW RAF-DB emotion recognition Accuracy Brain modeling Real-time systems Adaptation models Lighting Human computer interaction Facial features |
| dc.title.none.fl_str_mv | A Novel Transformer-Based Approach for Adult’s Facial Emotion Recognition |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Adult facial expression recognition (FER) is essential for human-computer interaction, mental health assessment, and social robotics applications because it improves user experiences and emotional well-being. This study presents a novel attention mechanism-based transformer approach designed to capture detailed patterns in facial features and dynamically focus on the most relevant regions for enhanced accuracy. Unlike conventional deep learning approaches, our method integrates an adaptive attention mechanism and dynamic token pruning, which optimizes computational efficiency while maintaining high accuracy. The model is evaluated on five widely used datasets: FER2013, CK+, AffectNet, RAF-DB, and AFEW. It achieves state-of-the-art performance, with accuracies of 98.67% on FER2013, 99.52% on CK+, 99.3% on AffectNet, 96.3% on AFEW, and 98.45% on RAF-DB. An ablation study further validates the contribution of each model component, and comparisons with CNN-based and transformer-based approaches confirm the effectiveness of the model. These findings establish the proposed method as a significant advancement in FER, which offers a scalable and efficient solution for real-world applications.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2025.3555510" target="_blank">https://dx.doi.org/10.1109/access.2025.3555510</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_361ba832583d29a64c5f08416c7f141e |
| identifier_str_mv | 10.1109/access.2025.3555510 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/30393349 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | A Novel Transformer-Based Approach for Adult’s Facial Emotion RecognitionUzma Nawaz (21980708)Zubair Saeed (19325647)Kamran Atif (22457845)Information and computing sciencesArtificial intelligenceComputer vision and multimedia computationMachine learningFacial emotion recognitiontransformersdeep learningFER2013CK+AffectNetAFEWRAF-DBemotion recognitionAccuracyBrain modelingReal-time systemsAdaptation modelsLightingHuman computer interactionFacial features<p dir="ltr">Adult facial expression recognition (FER) is essential for human-computer interaction, mental health assessment, and social robotics applications because it improves user experiences and emotional well-being. This study presents a novel attention mechanism-based transformer approach designed to capture detailed patterns in facial features and dynamically focus on the most relevant regions for enhanced accuracy. Unlike conventional deep learning approaches, our method integrates an adaptive attention mechanism and dynamic token pruning, which optimizes computational efficiency while maintaining high accuracy. The model is evaluated on five widely used datasets: FER2013, CK+, AffectNet, RAF-DB, and AFEW. It achieves state-of-the-art performance, with accuracies of 98.67% on FER2013, 99.52% on CK+, 99.3% on AffectNet, 96.3% on AFEW, and 98.45% on RAF-DB. An ablation study further validates the contribution of each model component, and comparisons with CNN-based and transformer-based approaches confirm the effectiveness of the model. These findings establish the proposed method as a significant advancement in FER, which offers a scalable and efficient solution for real-world applications.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2025.3555510" target="_blank">https://dx.doi.org/10.1109/access.2025.3555510</a></p>2025-04-04T06:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2025.3555510https://figshare.com/articles/journal_contribution/A_Novel_Transformer-Based_Approach_for_Adult_s_Facial_Emotion_Recognition/30393349CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/303933492025-04-04T06:00:00Z |
| spellingShingle | A Novel Transformer-Based Approach for Adult’s Facial Emotion Recognition Uzma Nawaz (21980708) Information and computing sciences Artificial intelligence Computer vision and multimedia computation Machine learning Facial emotion recognition transformers deep learning FER2013 CK+ AffectNet AFEW RAF-DB emotion recognition Accuracy Brain modeling Real-time systems Adaptation models Lighting Human computer interaction Facial features |
| status_str | publishedVersion |
| title | A Novel Transformer-Based Approach for Adult’s Facial Emotion Recognition |
| title_full | A Novel Transformer-Based Approach for Adult’s Facial Emotion Recognition |
| title_fullStr | A Novel Transformer-Based Approach for Adult’s Facial Emotion Recognition |
| title_full_unstemmed | A Novel Transformer-Based Approach for Adult’s Facial Emotion Recognition |
| title_short | A Novel Transformer-Based Approach for Adult’s Facial Emotion Recognition |
| title_sort | A Novel Transformer-Based Approach for Adult’s Facial Emotion Recognition |
| topic | Information and computing sciences Artificial intelligence Computer vision and multimedia computation Machine learning Facial emotion recognition transformers deep learning FER2013 CK+ AffectNet AFEW RAF-DB emotion recognition Accuracy Brain modeling Real-time systems Adaptation models Lighting Human computer interaction Facial features |