Convolutional Attention Based Mechanism for Facial Microexpression Recognition
Unanticipated and rapid change in facial expression are micro-expression (ME) that are hard to hide after an emotionally charged event. Facial microexpressions are transient and subtle, making identification challenging. Recognition of MEs are very crucial in the light of personal intention phase id...
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| Format: | Article |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10820184/ |
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| author | Hafiz Khizer Bin Talib Kaiwei Xu Yanlong Cao Yuan-Ping Xu Zhijie Xu Muhammad Zaman Adnan Akhunzada |
| author_facet | Hafiz Khizer Bin Talib Kaiwei Xu Yanlong Cao Yuan-Ping Xu Zhijie Xu Muhammad Zaman Adnan Akhunzada |
| author_sort | Hafiz Khizer Bin Talib |
| collection | DOAJ |
| description | Unanticipated and rapid change in facial expression are micro-expression (ME) that are hard to hide after an emotionally charged event. Facial microexpressions are transient and subtle, making identification challenging. Recognition of MEs are very crucial in the light of personal intention phase identification. Previous studies had challenges recognizing ME due to complicated spatiotemporal linkage in video data. Using the ConvMixer architecture, we Proposed a novel technique for facial microexpression identification based on convolutional attention mechanism. The research uses SAMM, SMIC, and CASME-II are benchmark datasets used to perform experiments. ConvMixer deployed to analyze the SAMM dataset where ConvMixer achieved an amazing 99.73% accuracy, 97.3% precision, 96.5% recall, and 99% F1-Score while 10-fold cross-validation. In addition, we extended our analysis to the CASME-II dataset, where ConvMixer attained an F1-Score of 99.4%, an accuracy of 99.12%, a precision of 98.3%, and a recall of 98.7%. These findings indicate that ConvMixer regularly outperforms other MER architectures, while capturing video specific and dynamic characteristics. ConvMixer architecture are good in capturing both spatial and temporal correlations and extracts spatial information using depthwise convolutions and channel mixing processes. High F1-Score, recall, precision, and accuracy across several datasets demonstrate the robustness and adaptability of the ConvMixer architecture. Finally, our findings show that the Convolutional Attention-Based Mechanism for facial microexpression recognition (CABM-FMER) works effectively for identifying facial MEs. |
| format | Article |
| id | doaj-art-49a054ac8b364a669d37238b0803073f |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-49a054ac8b364a669d37238b0803073f2025-02-11T00:01:41ZengIEEEIEEE Access2169-35362025-01-0113237322374710.1109/ACCESS.2024.352515110820184Convolutional Attention Based Mechanism for Facial Microexpression RecognitionHafiz Khizer Bin Talib0https://orcid.org/0009-0006-7683-8389Kaiwei Xu1Yanlong Cao2https://orcid.org/0000-0003-0383-6586Yuan-Ping Xu3Zhijie Xu4https://orcid.org/0000-0002-0524-5926Muhammad Zaman5https://orcid.org/0000-0001-6831-3589Adnan Akhunzada6https://orcid.org/0000-0001-8370-9290State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, ChinaState Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, ChinaState Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, ChinaSchool of Software Engineering, Chengdu University of Information Technology, Chengdu, ChinaSchool of Computing and Engineering, University of Huddersfield, Huddersfield, U.K.Department of Computer Science, COMSATS University Islamabad (CUI), Islamabad, PakistanDepartment of Data and Cybersecurity, College of Computing and IT, University of Doha for Science and Technology, Doha, QatarUnanticipated and rapid change in facial expression are micro-expression (ME) that are hard to hide after an emotionally charged event. Facial microexpressions are transient and subtle, making identification challenging. Recognition of MEs are very crucial in the light of personal intention phase identification. Previous studies had challenges recognizing ME due to complicated spatiotemporal linkage in video data. Using the ConvMixer architecture, we Proposed a novel technique for facial microexpression identification based on convolutional attention mechanism. The research uses SAMM, SMIC, and CASME-II are benchmark datasets used to perform experiments. ConvMixer deployed to analyze the SAMM dataset where ConvMixer achieved an amazing 99.73% accuracy, 97.3% precision, 96.5% recall, and 99% F1-Score while 10-fold cross-validation. In addition, we extended our analysis to the CASME-II dataset, where ConvMixer attained an F1-Score of 99.4%, an accuracy of 99.12%, a precision of 98.3%, and a recall of 98.7%. These findings indicate that ConvMixer regularly outperforms other MER architectures, while capturing video specific and dynamic characteristics. ConvMixer architecture are good in capturing both spatial and temporal correlations and extracts spatial information using depthwise convolutions and channel mixing processes. High F1-Score, recall, precision, and accuracy across several datasets demonstrate the robustness and adaptability of the ConvMixer architecture. Finally, our findings show that the Convolutional Attention-Based Mechanism for facial microexpression recognition (CABM-FMER) works effectively for identifying facial MEs.https://ieeexplore.ieee.org/document/10820184/Attention mechanismCABM-FMERConvMixermicro expression recognition |
| spellingShingle | Hafiz Khizer Bin Talib Kaiwei Xu Yanlong Cao Yuan-Ping Xu Zhijie Xu Muhammad Zaman Adnan Akhunzada Convolutional Attention Based Mechanism for Facial Microexpression Recognition IEEE Access Attention mechanism CABM-FMER ConvMixer micro expression recognition |
| title | Convolutional Attention Based Mechanism for Facial Microexpression Recognition |
| title_full | Convolutional Attention Based Mechanism for Facial Microexpression Recognition |
| title_fullStr | Convolutional Attention Based Mechanism for Facial Microexpression Recognition |
| title_full_unstemmed | Convolutional Attention Based Mechanism for Facial Microexpression Recognition |
| title_short | Convolutional Attention Based Mechanism for Facial Microexpression Recognition |
| title_sort | convolutional attention based mechanism for facial microexpression recognition |
| topic | Attention mechanism CABM-FMER ConvMixer micro expression recognition |
| url | https://ieeexplore.ieee.org/document/10820184/ |
| work_keys_str_mv | AT hafizkhizerbintalib convolutionalattentionbasedmechanismforfacialmicroexpressionrecognition AT kaiweixu convolutionalattentionbasedmechanismforfacialmicroexpressionrecognition AT yanlongcao convolutionalattentionbasedmechanismforfacialmicroexpressionrecognition AT yuanpingxu convolutionalattentionbasedmechanismforfacialmicroexpressionrecognition AT zhijiexu convolutionalattentionbasedmechanismforfacialmicroexpressionrecognition AT muhammadzaman convolutionalattentionbasedmechanismforfacialmicroexpressionrecognition AT adnanakhunzada convolutionalattentionbasedmechanismforfacialmicroexpressionrecognition |