Partially Occluded Face Expression Recognition with CBAM-Based Residual Network for Teaching Scene

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dc.contributor.authorBai, Y.
dc.contributor.authorChen, L.
dc.contributor.authorLi, M.
dc.contributor.authorWu, M.
dc.contributor.authorPedrycz, W.
dc.contributor.authorHirota, K.
dc.date.accessioned2024-05-19T14:33:23Z
dc.date.available2024-05-19T14:33:23Z
dc.date.issued2023
dc.departmentİstinye Üniversitesien_US
dc.description2023 China Automation Congress, CAC 2023 -- 17 November 2023 through 19 November 2023 -- -- 198194en_US
dc.description.abstractIn this paper, a deep residual network based on convolutional block attention module (CBAM) is proposed, which is utilized for feature extraction of partially occluded face expression data. The proposed method overcomes the problem of localized occlusion face feature extraction by focusing on the regions and channels containing important information in the occluded face data through CBAM. Multi-task cascaded convolutional networks (MTCNN) are firstly utilized to localize the key regions of face emotion, and then deep emotion features are extracted by CBAM-ResNet network. The final emotion labels are generated. The effectiveness of this paper's method is verified on the RAF-DB dataset and the occluded CK+ dataset. The experimental accuracy in the RAF-DB dataset is 76.3%, which is 3.74% and 1.64% higher than the accuracy produced by the method of RGBT, and the WLS-RF, respectively. Application experiments are carried out in the real teaching scenario, which verifies the applicability of the algorithm in the real teaching scene. © 2023 IEEE.en_US
dc.identifier.doi10.1109/CAC59555.2023.10450205
dc.identifier.endpage6057en_US
dc.identifier.isbn9798350303759
dc.identifier.scopus2-s2.0-85189324781en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage6052en_US
dc.identifier.urihttps://doi.org/10.1109/CAC59555.2023.10450205
dc.identifier.urihttps://hdl.handle.net/20.500.12713/4213
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofProceedings - 2023 China Automation Congress, CAC 2023en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240519_kaen_US
dc.subjectAttention Mechanismen_US
dc.subjectCbamen_US
dc.subjectPartially Occluded Facial Emotion Recognitionen_US
dc.subjectTeaching Sceneen_US
dc.titlePartially Occluded Face Expression Recognition with CBAM-Based Residual Network for Teaching Sceneen_US
dc.typeConference Objecten_US

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