Facial expression recognition using deep learning

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dc.authorid0000-0001-8776-3032
dc.contributor.authorShehu, Harisu Abdullahi
dc.contributor.authorSharif, Md Haidar
dc.contributor.authorUyaver, Şahin
dc.date.accessioned2021-12-30T08:30:27Z
dc.date.available2021-12-30T08:30:27Z
dc.date.issued2021
dc.departmentTAÜ, Fen Fakültesi, Enerji Bilimi ve Teknolojileri Bölümüen_US
dc.description.abstractFacial expression recognition has become an increasingly important area of research in recent years. Neural network-based methods have made amazing progress in performing recognition-based tasks, winning competitions set up by various data science communities, and achieving high performance on many datasets. Miscellaneous regularization methods have been utilized by various researchers to help combat over-fitting, to reduce training time, and to generalize their models. In this paper, by applying the Haar Cascade classifier to crop faces and focus on the region of interest, we hypothesize that we would attain a fast convergence without using the whole image to analyze facial expressions. We also apply label smoothing and analyze its effect on the databases of CK+, KDEF, and RAF. The ResNet model has been employed as an example of a neural network model. Label smoothing has demonstrated an improvement of the recognition accuracy up to 0.5% considering CK+ and the KDEF databases. While the application of Haar Cascade has shown to decrease the achieved accuracy on KDEF and RAF databases with a small margin, fast convergence of the model has been observed.
dc.identifier.citationShehu, H. A., Sharif, Md. H., Uyaver, Ş. & (2021) Facial expression recognition using deep learning.In AIP Conference Proceedings (Vol. 2334, No. 1, p. 070005). AIP Publishing LLC.
dc.identifier.doi10.1063/5.0042221
dc.identifier.issn0094-243X
dc.identifier.issue1en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12846/613
dc.identifier.volume2334en_US
dc.identifier.wosWOS:000664201400022
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.institutionauthorUyaver, Şahin
dc.language.isoen
dc.publisherAmerican Institute of Physics
dc.relation.ispartofFourth International Conference of Mathematical Sciences (ICMS 2020)
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectDeep Learningen_US
dc.subjectEmotionen_US
dc.subjectFacial Expressionen_US
dc.subjectHaar Cascadeen_US
dc.subjectLabel Smoothingen_US
dc.subjectRecognitionen_US
dc.subjectTiefes Lernenen_US
dc.subjectGesichtsausdrucken_US
dc.subjectHaarkaskadeen_US
dc.subjectEtikettenglättungen_US
dc.subjectErkennungen_US
dc.subjectDerin Öğrenmeen_US
dc.subjectDuyguen_US
dc.subjectYüz İfadesien_US
dc.subjectHaar Şelalesien_US
dc.subjectEtiket Yumuşatmaen_US
dc.titleFacial expression recognition using deep learning
dc.typeConference Object

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