Facial Expression Recognition in the Wild with Application in Robotics

Basit Öğe Kaydı
dc.authorscopusid57311290500
dc.authorscopusid57311290600
dc.authorscopusid36782998200
dc.authorscopusid57311107000
dc.authorscopusid57478991100
dc.contributor.authorHan, H.
dc.contributor.authorKaradeniz, O.
dc.contributor.authorSönmez, E.B.
dc.contributor.authorDalyan, T.
dc.contributor.authorSanoğlu, B.
dc.date.accessioned2024-07-18T20:17:12Z
dc.date.available2024-07-18T20:17:12Z
dc.date.issued2021
dc.description6th International Conference on Computer Science and Engineering, UBMK 2021 -- 15 September 2021 through 17 September 2021 -- -- 176826en_US
dc.description.abstractOne of the major problems with robot companions is their lack of credibility. Since emotions play a key role in human behaviour their implementation in virtual agents is a conditio sine-qua-non for realistic models. That is, correct classification of facial expressions in the wild is a necessary preprocessing step for implementing artificial empathy. The aim of this work is to implement a robust Facial Expression Recognition (FER) module into a robot. Considering the results of an empirical comparison among the most successful deep learning algorithms used for FER, this study fixes the state-ofthe-art performance of 75% on the FER2013 database with the ensemble method. With a single model, the best performance of 70.8% has been reached using the VGG16 architecture. Finally, the VGG16-based FER module has been been implemented into a robot and reached a performance of 70% when tested with wild expressive faces. © 2021 IEEEen_US
dc.identifier.doi10.1109/UBMK52708.2021.9558909
dc.identifier.endpage569en_US
dc.identifier.isbn9781665429085
dc.identifier.scopus2-s2.0-85125853804en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage565en_US
dc.identifier.urihttps://doi.org/10.1109/UBMK52708.2021.9558909
dc.identifier.urihttps://hdl.handle.net/11411/6439
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofProceedings - 6th International Conference on Computer Science and Engineering, UBMK 2021en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectDeep Learningen_US
dc.subjectFacial Expressions Classificationen_US
dc.subjectVirtual Humansen_US
dc.subjectBehavioral Researchen_US
dc.subjectDeep Learningen_US
dc.subjectE-Learningen_US
dc.subjectFace Recognitionen_US
dc.subjectReinforcement Learningen_US
dc.subjectRoboticsen_US
dc.subjectRobotsen_US
dc.subjectVirtual Realityen_US
dc.subjectDeep Learningen_US
dc.subjectFacial Expression Recognitionen_US
dc.subjectFacial Expressionsen_US
dc.subjectFacial Expressions Classificationsen_US
dc.subjectHuman Behaviorsen_US
dc.subjectPerformanceen_US
dc.subjectRealistic Modelen_US
dc.subjectRobot Companionen_US
dc.subjectVirtual Agenten_US
dc.subjectVirtual Humansen_US
dc.subjectLearning Algorithmsen_US
dc.titleFacial Expression Recognition in the Wild with Application in Roboticsen_US
dc.typeConference Objecten_US

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