PyFER: A Facial Expression Recognizer Based on Convolutional Neural Networks

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dc.authoridKabakus, Abdullah Talha/0000-0003-2181-4292
dc.authorwosidKabakus, Abdullah Talha/J-8361-2019
dc.contributor.authorKabakus, Abdullah Talha
dc.date.accessioned2021-12-01T18:47:24Z
dc.date.available2021-12-01T18:47:24Z
dc.date.issued2020
dc.departmentDÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractFacial expression recognition (FER), one of the most trending research areas of the Human-Machine Interaction, is the task of detecting emotions by analyzing facial expressions and this analysis plays a critical role as it conveys the clearest information regarding the emotions of people. Despite the fact that the traditional machine learning algorithms produce high accuracies for similar tasks, they lack to detect emotions of faces, which are captured in a spontaneous manner (a.k.a. in the wild) or in different poses or environmental conditions. In this article, a novel convolutional neural network architecture, namely, PyFER, is proposed to address the FER problem, of which the efficiency was revealed thanks to the experiments conducted on a widely-used benchmark dataset. According to the experimental results, the accuracy of PyFER was calculated to be as high as 96.3% on a de-facto standard dataset, namely, CK+, and all facial expressions, except for happiness, were correctly detected by PyFER, which is encouraging for future studies. 16.67% of the images that actually represented the facial expression happiness were misdetected as the facial expression fear. The experimental results confirmed that the proposed neural network architecture is fast enough to be integrated into real-time FER applications as it was able to complete the analysis of a given photo for an average of 12.8 milliseconds, which is in the tolerable limit to latency for real-time applications.en_US
dc.identifier.doi10.1109/ACCESS.2020.3012703
dc.identifier.endpage142249en_US
dc.identifier.issn2169-3536
dc.identifier.scopus2-s2.0-85089952253en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage142243en_US
dc.identifier.urihttps://doi.org/10.1109/ACCESS.2020.3012703
dc.identifier.urihttps://hdl.handle.net/20.500.12684/10258
dc.identifier.volume8en_US
dc.identifier.wosWOS:000560529800001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorKabakus, Abdullah Talha
dc.language.isoenen_US
dc.publisherIeee-Inst Electrical Electronics Engineers Incen_US
dc.relation.ispartofIeee Accessen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectFeature extractionen_US
dc.subjectComputer architectureen_US
dc.subjectConvolutional neural networksen_US
dc.subjectBiological neural networksen_US
dc.subjectFace recognitionen_US
dc.subjectTask analysisen_US
dc.subjectArtificial intelligenceen_US
dc.subjectartificial neural networksen_US
dc.subjectbackpropagationen_US
dc.subjectmulti-layer neural networken_US
dc.subjectneural networksen_US
dc.subjectsupervised learningen_US
dc.subjectReal-Timeen_US
dc.subjectSystemen_US
dc.titlePyFER: A Facial Expression Recognizer Based on Convolutional Neural Networksen_US
dc.typeArticleen_US

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