Micro-Expression Recognition using Convolutional Variational Attention Transformer (ConVAT) with Multihead Attention Mechanism
<p dir="ltr">Micro-Expression Recognition is crucial in various fields such as behavioral analysis, security, and psychological studies, offering valuable insights into subtle and often concealed emotional states. Despite significant advancements in deep learning models, challenges p...
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2025
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| _version_ | 1864513549976469504 |
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| author | Hafiz Khizer bin Talib (20571467) |
| author2 | Kaiwei Xu (822777) Yanlong Cao (4737243) Yuan Ping Xu (20571470) Zhijie Xu (3929918) Muhammad Zaman (66868) Adnan Akhunzada (20151648) |
| author2_role | author author author author author author |
| author_facet | Hafiz Khizer bin Talib (20571467) Kaiwei Xu (822777) Yanlong Cao (4737243) Yuan Ping Xu (20571470) Zhijie Xu (3929918) Muhammad Zaman (66868) Adnan Akhunzada (20151648) |
| author_role | author |
| dc.creator.none.fl_str_mv | Hafiz Khizer bin Talib (20571467) Kaiwei Xu (822777) Yanlong Cao (4737243) Yuan Ping Xu (20571470) Zhijie Xu (3929918) Muhammad Zaman (66868) Adnan Akhunzada (20151648) |
| dc.date.none.fl_str_mv | 2025-01-16T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/ACCESS.2025.3530114 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Micro-Expression_Recognition_using_Convolutional_Variational_Attention_Transformer_ConVAT_with_Multihead_Attention_Mechanism/28224824 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Control engineering, mechatronics and robotics Information and computing sciences Artificial intelligence Computer vision and multimedia computation Machine learning Psychology Biological psychology ConVAT Convolutional neural networks LOSO cross-validation Micro-expression recognition Multi-head Attention |
| dc.title.none.fl_str_mv | Micro-Expression Recognition using Convolutional Variational Attention Transformer (ConVAT) with Multihead Attention Mechanism |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Micro-Expression Recognition is crucial in various fields such as behavioral analysis, security, and psychological studies, offering valuable insights into subtle and often concealed emotional states. Despite significant advancements in deep learning models, challenges persist in accurately handling the nuanced and fleeting nature of micro-expressions, particularly when applied across diverse datasets with varied expressions. Existing models often struggle with precision and adaptability, leading to inconsistent recognition performance. To address these limitations, we propose the Convolutional Variational Attention Transformer (ConVAT), a novel model that leverages a multi-head attention mechanism integrated with convolutional networks, optimized specifically for detailed micro-expression analysis. Our methodology employs the Leave-One-Subject-Out (LOSO) cross-validation technique across three widely used datasets: SAMM, CASME II, and SMIC. The results demonstrate the effectiveness of ConVAT, achieving impressive performance with 98.73% accuracy on the SAMM dataset, 97.95% on the SMIC dataset, and 97.65% on CASME II. These outcomes not only surpass current state-of-the-art benchmarks but also highlight ConVAT’s robustness and reliability in capturing micro-expressions, marking a significant advancement toward developing sophisticated automated systems for real-world applications in micro-expression recognition.</p><h2>Other Information:</h2><p dir="ltr">Published in: <a href="https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639" target="_blank">IEEE Access</a> <br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://doi.org/10.1109/access.2025.3530114" rel="noreferrer" target="_blank">https://doi.org/10.1109/access.2025.3530114</a></p><p dir="ltr"><br></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_1fd4dc1ecee77d8153f770fef0677c6f |
| identifier_str_mv | 10.1109/ACCESS.2025.3530114 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/28224824 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Micro-Expression Recognition using Convolutional Variational Attention Transformer (ConVAT) with Multihead Attention MechanismHafiz Khizer bin Talib (20571467)Kaiwei Xu (822777)Yanlong Cao (4737243)Yuan Ping Xu (20571470)Zhijie Xu (3929918)Muhammad Zaman (66868)Adnan Akhunzada (20151648)EngineeringControl engineering, mechatronics and roboticsInformation and computing sciencesArtificial intelligenceComputer vision and multimedia computationMachine learningPsychologyBiological psychologyConVATConvolutional neural networksLOSO cross-validationMicro-expression recognitionMulti-head Attention<p dir="ltr">Micro-Expression Recognition is crucial in various fields such as behavioral analysis, security, and psychological studies, offering valuable insights into subtle and often concealed emotional states. Despite significant advancements in deep learning models, challenges persist in accurately handling the nuanced and fleeting nature of micro-expressions, particularly when applied across diverse datasets with varied expressions. Existing models often struggle with precision and adaptability, leading to inconsistent recognition performance. To address these limitations, we propose the Convolutional Variational Attention Transformer (ConVAT), a novel model that leverages a multi-head attention mechanism integrated with convolutional networks, optimized specifically for detailed micro-expression analysis. Our methodology employs the Leave-One-Subject-Out (LOSO) cross-validation technique across three widely used datasets: SAMM, CASME II, and SMIC. The results demonstrate the effectiveness of ConVAT, achieving impressive performance with 98.73% accuracy on the SAMM dataset, 97.95% on the SMIC dataset, and 97.65% on CASME II. These outcomes not only surpass current state-of-the-art benchmarks but also highlight ConVAT’s robustness and reliability in capturing micro-expressions, marking a significant advancement toward developing sophisticated automated systems for real-world applications in micro-expression recognition.</p><h2>Other Information:</h2><p dir="ltr">Published in: <a href="https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639" target="_blank">IEEE Access</a> <br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://doi.org/10.1109/access.2025.3530114" rel="noreferrer" target="_blank">https://doi.org/10.1109/access.2025.3530114</a></p><p dir="ltr"><br></p>2025-01-16T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/ACCESS.2025.3530114https://figshare.com/articles/journal_contribution/Micro-Expression_Recognition_using_Convolutional_Variational_Attention_Transformer_ConVAT_with_Multihead_Attention_Mechanism/28224824CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/282248242025-01-16T03:00:00Z |
| spellingShingle | Micro-Expression Recognition using Convolutional Variational Attention Transformer (ConVAT) with Multihead Attention Mechanism Hafiz Khizer bin Talib (20571467) Engineering Control engineering, mechatronics and robotics Information and computing sciences Artificial intelligence Computer vision and multimedia computation Machine learning Psychology Biological psychology ConVAT Convolutional neural networks LOSO cross-validation Micro-expression recognition Multi-head Attention |
| status_str | publishedVersion |
| title | Micro-Expression Recognition using Convolutional Variational Attention Transformer (ConVAT) with Multihead Attention Mechanism |
| title_full | Micro-Expression Recognition using Convolutional Variational Attention Transformer (ConVAT) with Multihead Attention Mechanism |
| title_fullStr | Micro-Expression Recognition using Convolutional Variational Attention Transformer (ConVAT) with Multihead Attention Mechanism |
| title_full_unstemmed | Micro-Expression Recognition using Convolutional Variational Attention Transformer (ConVAT) with Multihead Attention Mechanism |
| title_short | Micro-Expression Recognition using Convolutional Variational Attention Transformer (ConVAT) with Multihead Attention Mechanism |
| title_sort | Micro-Expression Recognition using Convolutional Variational Attention Transformer (ConVAT) with Multihead Attention Mechanism |
| topic | Engineering Control engineering, mechatronics and robotics Information and computing sciences Artificial intelligence Computer vision and multimedia computation Machine learning Psychology Biological psychology ConVAT Convolutional neural networks LOSO cross-validation Micro-expression recognition Multi-head Attention |