Facial landmark based region of interest localization for deep facial expression recognition

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dc.authorid0000-0002-4076-5230en_US
dc.contributor.authorSöylemez, Ömer Faruk
dc.contributor.authorErgen, Burhan
dc.date.accessioned2023-01-24T09:01:31Z
dc.date.available2023-01-24T09:01:31Z
dc.date.issued2022en_US
dc.departmentDicle Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractAutomated facial expression recognition has gained much attention in the last years due to growing application areas such as computer animated agents, sociable robots and human computer interaction. The realization of a reliable facial expression recognition system through machine learning is still a challenging task particularly on databases with large number of images. Convolutional Neural Network (CNN) architectures have been proposed to deal with large numbers of training data for better accuracy. For CNNs, a task related best achieving architectural structure does not exist. In addition, the representation of the input image is equivalently important as the architectural structure and the training data. Therefore, this study focuses on the performances of various CNN architectures trained by different region of interests of the same input data. Experiments are performed on three distinct CNN architectures with three different crops of the same dataset. Results show that by appropriately localizing the facial region and selecting the correct CNN architecture it is possible to boost the recognition rate from 84% to 98% while decreasing the training time for proposed CNN architectures.en_US
dc.identifier.citationSöylemez, Ö.F. ve Ergen, B. (2022). Facial landmark based region of interest localization for deep facial expression recognition. Tehnički Vjesnik - Technical Gazette, 29(1), 38-44en_US
dc.identifier.doi10.17559/TV-20200423145443
dc.identifier.endpage44en_US
dc.identifier.issn1330-3651
dc.identifier.issn1848-6339
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-85125627222
dc.identifier.scopusqualityQ3
dc.identifier.startpage38en_US
dc.identifier.urihttps://hrcak.srce.hr/269480
dc.identifier.urihttps://hdl.handle.net/11468/11220
dc.identifier.volume29en_US
dc.identifier.wosWOS:000739663500006
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorSöylemez, Ömer Faruk
dc.language.isoenen_US
dc.publisherUniv Osijeken_US
dc.relation.ispartofTehni?ki Vjesnik - Technical Gazette
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectConvolutional neural networksen_US
dc.subjectDeep learningen_US
dc.subjectFacial expression recognitionen_US
dc.titleFacial landmark based region of interest localization for deep facial expression recognitionen_US
dc.titleFacial landmark based region of interest localization for deep facial expression recognition
dc.typeArticleen_US

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