Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14365/3511
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dc.contributor.authorOzkara C.-
dc.contributor.authorEkim P.O.-
dc.date.accessioned2023-06-16T14:59:33Z-
dc.date.available2023-06-16T14:59:33Z-
dc.date.issued2022-
dc.identifier.isbn9.78167E+12-
dc.identifier.urihttps://doi.org/10.1109/ASYU56188.2022.9925465-
dc.identifier.urihttps://hdl.handle.net/20.500.14365/3511-
dc.description2022 Innovations in Intelligent Systems and Applications Conference, ASYU 2022 -- 7 September 2022 through 9 September 2022 -- 183936en_US
dc.description.abstractThis project aims to review the most popular deep learning algorithms and their performances in camera systems based on real-time facial emotion recognition and suggest a new model for future applications. Firstly, convolutional neural network (CNN) algorithms that recognize human emotions, such as AlexNet, GoogleNet, and VGG19, are investigated according to their performances. Then, the CNN algorithm with the best numerical performance is chosen for enhancement. After, the new hybrid model is constructed via chosen CNN and long short-term memory (LSTM). Lastly, the proposed model and face images achieved from the camera are combined to simulate real-time application. © 2022 IEEE.en_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofProceedings - 2022 Innovations in Intelligent Systems and Applications Conference, ASYU 2022en_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectCNNen_US
dc.subjectface detectionen_US
dc.subjectfacial expression recognitionen_US
dc.subjecthybrid modelen_US
dc.subjectLSTMen_US
dc.subjectCamerasen_US
dc.subjectConvolutional neural networksen_US
dc.subjectEmotion Recognitionen_US
dc.subjectFace recognitionen_US
dc.subjectReal time systemsen_US
dc.subjectSpeech recognitionen_US
dc.subjectConvolutional neural networken_US
dc.subjectEmotion recognitionen_US
dc.subjectFaces detectionen_US
dc.subjectFacial emotionsen_US
dc.subjectFacial expression recognitionen_US
dc.subjectHybrid modelen_US
dc.subjectNeural networks algorithmsen_US
dc.subjectPerformanceen_US
dc.subjectReal- timeen_US
dc.subjectVisualization systemen_US
dc.subjectLong short-term memoryen_US
dc.titleReal-Time Facial Emotion Recognition for Visualization Systemsen_US
dc.typeConference Objecten_US
dc.identifier.doi10.1109/ASYU56188.2022.9925465-
dc.identifier.scopus2-s2.0-85142765138en_US
dc.authorscopusid57982266100-
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.identifier.scopusqualityN/A-
dc.identifier.wosqualityN/A-
item.grantfulltextreserved-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
item.openairetypeConference Object-
item.fulltextWith Fulltext-
item.languageiso639-1en-
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
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