Micro-Expression Recognition by Using CNN Features with PSO Algorithm and SVM Methods

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dc.contributor.authorUzun, Mehmet Zahit
dc.contributor.authorCelik, Yuksel
dc.contributor.authorBasaran, Erdal
dc.date.accessioned2024-09-29T16:06:37Z
dc.date.available2024-09-29T16:06:37Z
dc.date.issued2022
dc.departmentKarabük Üniversitesien_US
dc.description.abstractThis study proposes a framework for defining ME expressions, in which preprocessing, feature extraction with deep learning, feature selection with an optimization algorithm, and classification methods are used. CASME-II, SMIC-HS, and SAMM, which are among the most used ME datasets in the literature, were combined to overcome the under-sampling problem caused by the datasets. In the preprocessing stage, onset, and apex frames in each video clip in datasets were detected, and optical flow images were obtained from the frames using the FarneBack method. The features of these obtained images were extracted by applying AlexNet, VGG16, MobilenetV2, EfficientNet, Squeezenet from CNN models. Then, combining the image features obtained from all CNN models. And then, the ones which are the most distinctive features were selected with the Particle Swarm Optimization (PSO) algorithm. The new feature set obtained was divided into classes positive, negative, and surprise using SVM. As a result, its success has been demonstrated with an accuracy rate of 0.8784 obtained in our proposed ME framework.en_US
dc.identifier.doi10.18280/ts.390526
dc.identifier.endpage1693en_US
dc.identifier.issn0765-0019
dc.identifier.issn1958-5608
dc.identifier.issue5en_US
dc.identifier.scopus2-s2.0-85150180977en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage1685en_US
dc.identifier.urihttps://doi.org/10.18280/ts.390526
dc.identifier.urihttps://hdl.handle.net/20.500.14619/6930
dc.identifier.volume39en_US
dc.identifier.wosWOS:000907630800019en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherInt Information & Engineering Technology Assocen_US
dc.relation.ispartofTraitement Du Signalen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectCNNen_US
dc.subjectFarneBacken_US
dc.subjectmicro expressionen_US
dc.subjectoptical flowen_US
dc.subjectPSOen_US
dc.subjectSVMen_US
dc.titleMicro-Expression Recognition by Using CNN Features with PSO Algorithm and SVM Methodsen_US
dc.typeArticleen_US

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