Video-based emotion recognition in the wild using deep transfer learning and score fusion

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dc.authorid0000-0001-7947-5508
dc.authorid0000-0001-6342-428X
dc.authorid0000-0001-6342-428X
dc.authorid0000-0001-8270-9969
dc.authorscopusid36241785000
dc.authorscopusid56565406500
dc.authorscopusid7006556254
dc.authorwosidKAYA, Heysem/V-4493-2019
dc.authorwosidSalah, Albert Ali/ABH-5561-2020
dc.authorwosidSalah, Albert Ali/E-5820-2013
dc.contributor.authorKaya, Heysem
dc.contributor.authorGürpınar, Furkan
dc.contributor.authorSalah, Albert Ali
dc.date.accessioned2022-05-11T14:15:52Z
dc.date.available2022-05-11T14:15:52Z
dc.date.issued2017
dc.departmentFakülteler, Çorlu Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractMultimodal recognition of affective states is a difficult problem, unless the recording conditions are carefully controlled. For recognition in the wild, large variances in face pose and illumination, cluttered backgrounds, occlusions, audio and video noise, as well as issues with subtle cues of expression are some of the issues to target. In this paper, we describe a multimodal approach for video-based emotion recognition in the wild. We propose using summarizing functionals of complementary visual descriptors for video modeling. These features include deep convolutional neural network (CNN) based features obtained via transfer learning, for which we illustrate the importance of flexible registration and fine-tuning. Our approach combines audio and visual features with least squares regression based classifiers and weighted score level fusion. We report state-of-the-art results on the EmotiW Challenge for in the wild facial expression recognition. Our approach scales to other problems, and ranked top in the ChaLearn-LAP First Impressions Challenge 2016 from video clips collected in the wild. (C) 2017 Elsevier B.V. All rights reserved.
dc.identifier.doi10.1016/j.imavis.2017.01.012
dc.identifier.endpage75
dc.identifier.issn0262-8856
dc.identifier.issn1872-8138
dc.identifier.scopus2-s2.0-85012924090
dc.identifier.scopusqualityQ2
dc.identifier.startpage66
dc.identifier.urihttps://doi.org/10.1016/j.imavis.2017.01.012
dc.identifier.urihttps://hdl.handle.net/20.500.11776/6102
dc.identifier.volume65
dc.identifier.wosWOS:000412618500008
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorKaya, Heysem
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofImage and Vision Computing
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectEmotiW
dc.subjectEmotion recognition in the wild
dc.subjectMultimodal fusion
dc.subjectConvolutional neural networks
dc.subjectKernel extreme learning machine
dc.subjectPartial least squares
dc.subjectMachine
dc.titleVideo-based emotion recognition in the wild using deep transfer learning and score fusion
dc.typeArticle

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