Addressing Class Imbalance in Facial Emotion Recognition

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dc.contributor.authorGhafourian Bolori Mashhad, Sarvenaz
dc.contributor.supervisorBaniasadi, Amirali
dc.date.accessioned2021-12-08T23:12:44Z
dc.date.available2021-12-08T23:12:44Z
dc.date.copyright2021en_US
dc.date.issued2021-12-08
dc.degree.departmentDepartment of Electrical and Computer Engineeringen_US
dc.degree.levelMaster of Engineering M.Eng.en_US
dc.description.abstractThe wide usage of computer vision in many perspectives has been attracted in the recent years. One of the areas of computer vision that has been studied is facial emotion recognition, which plays a crucial role in the interpersonal communication. This work demonstrates the advances could be made in this eld. This work tackles the problem of intraclass variances in the face images of emotion recognition datasets. We test the system on an augmented dataset including CK+, EMOTIC, and KDEF dataset samples, which increase the intraclass variances in the face images of our dataset. The proposed method is based on SMOTETomek.en_US
dc.description.scholarlevelGraduateen_US
dc.identifier.urihttp://hdl.handle.net/1828/13571
dc.language.isoenen_US
dc.rightsAvailable to the World Wide Weben_US
dc.titleAddressing Class Imbalance in Facial Emotion Recognitionen_US
dc.typeprojecten_US

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