A Study on Facial Expression Recognition

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dc.authoridBattini Sonmez, Elena/0000-0003-0090-984X
dc.authorwosidBattini Sonmez, Elena/AAZ-6358-2021
dc.contributor.authorBattini Sonmez, Elena
dc.date.accessioned2024-07-18T20:50:56Z
dc.date.available2024-07-18T20:50:56Z
dc.date.issued2017
dc.departmentİstanbul Bilgi Üniversitesien_US
dc.description.abstractThis study focuses on the issue of automatic facial expression recognition on little databases of 2D faces. Convolutional Neural Networks (CNN) is a new classification technique, which reaches the state of the art on big databases; however, the use of CNN with a scarce number of samples is still an open challenge. Following the classical machine learning approach, we considered different combination of feature extraction and classifiers, and we compared their performances with special designed CNN. Our results show that CNN outperforms the other classifiers in the close system experiment; however, in the more challenging open system experimental setup the Sparse Representation based Classifier is more successful.en_US
dc.identifier.endpage27en_US
dc.identifier.issn2147-1762
dc.identifier.issue3en_US
dc.identifier.scopus2-s2.0-85040001986en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage19en_US
dc.identifier.urihttps://hdl.handle.net/11411/8305
dc.identifier.volume30en_US
dc.identifier.wosWOS:000418815100002en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherGazi Univen_US
dc.relation.ispartofGazi University Journal of Scienceen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectLittle Databasesen_US
dc.subjectFacial Expression Recognitionen_US
dc.subjectSparse Representation Based Classifieren_US
dc.subjectConvolutional Neural Networken_US
dc.subjectFace Recognitionen_US
dc.titleA Study on Facial Expression Recognition
dc.typeArticle

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