Facial Emotion Recognition on a Dataset Using Convolutional Neural Network

dc.contributor.authorTumen, Vedat
dc.contributor.authorSoylemez, Omer Faruk
dc.contributor.authorErgen, Burhan
dc.date.accessioned2024-04-24T17:33:57Z
dc.date.available2024-04-24T17:33:57Z
dc.date.issued2017
dc.departmentDicle Üniversitesien_US
dc.description2017 International Artificial Intelligence and Data Processing Symposium (IDAP) -- SEP 16-17, 2017 -- Malatya, TURKEYen_US
dc.description.abstractNowadays, deep learning is a technique that takes place in many computer vision related applications and studies. While it is put in the practice mostly on content based image retrieval, there is still room for improvement by employing it in diverse computer vision applications. In this study, we aimed to build a Convolutional Neural Network (CNN) based Facial Expression Recognition System (FER), in order to automatically classify expressions presented in Facial Expression Recognition (FER2013) database. Our presented CNN achieved % 57.1 success rate on FER2013 database.en_US
dc.description.sponsorshipIEEE Turkey Sect,Anatolian Scien_US
dc.identifier.isbn978-1-5386-1880-6
dc.identifier.scopus2-s2.0-85039912842
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://hdl.handle.net/11468/20902
dc.identifier.wosWOS:000426868700121
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isotren_US
dc.publisherIeeeen_US
dc.relation.ispartof2017 International Artificial Intelligence and Data Processing Symposium (Idap)
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectDeep Learningen_US
dc.subjectFacial Expression Recognitionen_US
dc.subjectImage Classificationen_US
dc.subjectConvolution Neural Networksen_US
dc.titleFacial Emotion Recognition on a Dataset Using Convolutional Neural Networken_US
dc.titleFacial Emotion Recognition on a Dataset Using Convolutional Neural Network
dc.typeConference Objecten_US

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