Facial expression recognition using deep learning

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dc.authorid0000-0002-9689-3290en_US
dc.authorid0000-0001-7235-6004en_US
dc.authorid0000-0001-8776-3032en_US
dc.contributor.authorShehu, Harisu Abdullahi
dc.contributor.authorSharif, Md. Haidar
dc.contributor.authorUyaver, Sahin
dc.date.accessioned2024-07-12T20:47:16Z
dc.date.available2024-07-12T20:47:16Z
dc.date.issued2021en_US
dc.departmentFakülteler, İnsan ve Toplum Bilimleri Fakültesi, Matematik Bölümüen_US
dc.description.abstractt. Facial expression recognition has become an increasingly important area of research in recent years. Neural networkbased 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.en_US
dc.identifier.citationShehu, H. A., Sharif, Md. H. ve Uyaver, S. (2021). Facial expression recognition using deep learning. Fourth International Conference of Mathematical Sciences, Maltepe Üniversitesi. s. 1-5.en_US
dc.identifier.endpage5en_US
dc.identifier.isbn978-0-7354-4078-4
dc.identifier.startpage1en_US
dc.identifier.urihttps://aip.scitation.org/doi/10.1063/5.0042221
dc.identifier.urihttps://hdl.handle.net/20.500.12415/1967
dc.language.isoenen_US
dc.publisherMaltepe Üniversitesien_US
dc.relation.ispartofFourth International Conference of Mathematical Sciencesen_US
dc.relation.isversionof10.1063/5.0042221en_US
dc.relation.publicationcategoryUluslararası Konferans Öğesi - Başka Kurum Yazarıen_US
dc.rightsCC0 1.0 Universal*
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/*
dc.snmzKY07387
dc.subjectDeep learningen_US
dc.subjectEmotionen_US
dc.subjectFacial expressionen_US
dc.subjectHaar cascadeen_US
dc.subjectLabel smoothingen_US
dc.subjectRecognitionen_US
dc.titleFacial expression recognition using deep learningen_US
dc.typeConference Object
dspace.entity.typePublication

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