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A Performance Study on Emotion Models Detection Accuracy in a Pandemic Environment

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dc.contributor.authorSaravanan P.en_US
dc.contributor.authorRavindran S.en_US
dc.contributor.authorWeng L.Y.en_US
dc.contributor.authorMohamed Sahari K.S.B.en_US
dc.contributor.authorAnuar A.B.en_US
dc.contributor.authorAbdul Jalal M.F.B.en_US
dc.contributor.authorMohamad Rafaai Z.F.B.en_US
dc.contributor.authorRaventhran P.N.A./P.en_US
dc.contributor.authorMohd Radzi H.en_US
dc.contributor.authorYussof S.en_US
dc.contributor.authorid57361356600en_US
dc.contributor.authorid57361613900en_US
dc.contributor.authorid26326032700en_US
dc.contributor.authorid57218170038en_US
dc.contributor.authorid13609166500en_US
dc.contributor.authorid57218163299en_US
dc.contributor.authorid57218873528en_US
dc.contributor.authorid57361614200en_US
dc.contributor.authorid57361547000en_US
dc.contributor.authorid16023225600en_US
dc.date.accessioned2023-05-29T09:10:45Z
dc.date.available2023-05-29T09:10:45Z
dc.date.issued2021
dc.descriptionComputers; Covid19; Deep learning; Detection accuracy; Emotion; Emotion models; Full faces; High-accuracy; Pandemic; Performance study; Skin patch; Deep learningen_US
dc.description.abstractThis paper studies emotion detection using deep learning on the prevalent usage of face masks in the Covid-19 pandemic. Internet repository data Karolinska Directed Emotional Faces (KDEF) [1] was used as a base database, in which it was segmented into different portions of the face, such as forehead patch, eye patch, and skin patch to be representing segments of the face covered or exposed by the mask were transfer learned to an Inception v3 model. Results show that the full-face model had the highest accuracy 74.68% followed by the skin patch (area occluded by the mask) 65.09%. The models trained on full-face were then used to inference the different face segments/patches that showed poor inferencing results. However, certain emotions are more distinct around the eye region. Therefore, this paper concludes that upper segmented faces result in higher accuracy for training models over full faces, yet future research needs to be done on additional occlusion near the eye section. � 2021, Springer Nature Switzerland AG.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/978-3-030-90235-3_28
dc.identifier.epage331
dc.identifier.scopus2-s2.0-85120526493
dc.identifier.spage322
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85120526493&doi=10.1007%2f978-3-030-90235-3_28&partnerID=40&md5=878ac9a60ff45343883882122a4e00be
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26459
dc.identifier.volume13051 LNCS
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.sourceScopus
dc.sourcetitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.titleA Performance Study on Emotion Models Detection Accuracy in a Pandemic Environmenten_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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