Show simple item record

dc.contributor.advisorDibeklioğlu, Hamdi
dc.contributor.authorMandıra, Burak
dc.date.accessioned2021-10-07T08:26:22Z
dc.date.available2021-10-07T08:26:22Z
dc.date.copyright2021-09
dc.date.issued2021-09
dc.date.submitted2021-10-06
dc.identifier.urihttp://hdl.handle.net/11693/76593
dc.descriptionCataloged from PDF version of article.en_US
dc.descriptionThesis (Master's): Bilkent University, Department of Computer Engineering, İhsan Doğramacı Bilkent University, 2021.en_US
dc.descriptionIncludes bibliographical references (leaves 60-71).en_US
dc.description.abstractWe encounter with deceptive behavior in our daily lives, almost everyday. Even the most reliable ones among us can sometimes be deceptive either deliberately or unintentionally. Since people are not successful at detecting lies most of the time, it is necessary to use an automated deception detection system particularly in high-stake scenarios such as court trials. We propose a fully automated personality-aware deception detection model that uses videos as input. To our knowledge, we are the first to consider analyzing the personality of subjects in a deception detection task. The proposed model is a multimodal approach where it uses both facial expression and voice related cues in addition to personality traits of subjects in its analyses. After personality traits are extracted, they are combined with deception features which are based on expression cues. Deception, voice, and personality modules are constituted from the spatiotemporal architectures such as 3D-ResNext and CNN-GRU to better comprehend the temporal dynamics of the input. Finally, it combines expression and voice modalities using a GRU based fusion model. The evaluation of the proposed model is performed on Real Life Trials dataset that uses the records of court trials from real life. The results suggest that the use of personality traits facilitates the deception detection task. When the personality features are employed in addition to deception features, there is up to 20.4% (relative) improvement on the performance of the deception module. When the voice related cues are also considered upon that, we obtain 15.4% (relative) improvement additionally.en_US
dc.description.statementofresponsibilityby Burak Mandıraen_US
dc.format.extentxii, 71 leaves : illustrations, graphics, charts (colors) ; 30 cm.en_US
dc.language.isoEnglishen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectDeception detectionen_US
dc.subjectPersonality analysisen_US
dc.subjectMultimodal fusionen_US
dc.titlePersonality-aware deception detection from behavioral cuesen_US
dc.title.alternativeDavranışsal özelliklerden kişilik duyarlı aldatma algılamaen_US
dc.typeThesisen_US
dc.departmentDepartment of Computer Engineeringen_US
dc.publisherBilkent Universityen_US
dc.description.degreeM.S.en_US
dc.identifier.itemidB132834
dc.embargo.release2022-04-04


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record