Deep Feature Selection for Facial Emotion Recognition Based on BPSO and SVM

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dc.authoridHanbay, Davut/0000-0003-2271-7865
dc.authoridari, ali/0000-0002-5071-6790
dc.authoridOzdemir, Mehmet Fatih/0000-0003-3563-054X
dc.authorwosidHanbay, Davut/AAG-8511-2019
dc.contributor.authorDonuk, Kenan
dc.contributor.authorAri, Ali
dc.contributor.authorOzdemir, Mehmet Fatih
dc.contributor.authorHanbay, Davut
dc.date.accessioned2024-08-04T20:11:44Z
dc.date.available2024-08-04T20:11:44Z
dc.date.issued2023
dc.departmentİnönü Üniversitesien_US
dc.description.abstractFacial expressions, which are important social communication tools in our daily life, provide important information about the mental state of people. Research is being done to obtain this information accurately. The importance of these researchs in the field of human-computer interaction is increasing. Many methods have been used for the recognition of universal facial expressions such as neutral, happiness, surprise, sadness, anger, disgust, and fear by intelligent systems with high accuracy. Emotion recognition is an example of difficult classification due to factors such as ambient light, age, race, gender, and facial position. In this article, a 3-stage system is proposed for emotion detection from facial images. In the first stage, the CNN-based network is trained with the Fer+ dataset. The Binary Particle Swarm Optimization algorithm is applied for feature selection to the feature vector in the fully connected layer of the CNN network trained in the second stage. Selected features are classified by Support Vector Machine. The performance of the proposed system has been tested with the Fer+ dataset. As a result of the test, 85.74% accuracy was measured. The results show that the combination of BPSO and SVM contributes to the classification accuracy and speed of the FER+ dataset.en_US
dc.description.sponsorshipInonu University Scientific Research Projects Coordination Unit (BAP) [FDK-2020-2110]en_US
dc.description.sponsorshipThis study was supported by Inonu University Scientific Research Projects Coordination Unit (BAP) with the project coded FDK-2020-2110.en_US
dc.identifier.doi10.2339/politeknik.992720
dc.identifier.endpage142en_US
dc.identifier.issn1302-0900
dc.identifier.issn2147-9429
dc.identifier.issue1en_US
dc.identifier.startpage131en_US
dc.identifier.trdizinid1236285en_US
dc.identifier.urihttps://doi.org/10.2339/politeknik.992720
dc.identifier.urihttps://search.trdizin.gov.tr/yayin/detay/1236285
dc.identifier.urihttps://hdl.handle.net/11616/92959
dc.identifier.volume26en_US
dc.identifier.wosWOS:001022165400012en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakTR-Dizinen_US
dc.language.isoenen_US
dc.publisherGazi Univen_US
dc.relation.ispartofJournal of Polytechnic-Politeknik Dergisien_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectFacial emotion recognitionen_US
dc.subjectconvolutional neural networken_US
dc.subjectbinary particle swarm optimizationen_US
dc.subjectsupport vector machineen_US
dc.titleDeep Feature Selection for Facial Emotion Recognition Based on BPSO and SVMen_US
dc.typeArticleen_US

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