Deception Detection Using a Convolutional Neural Network

dc.contributor.advisorDr. Guclu, Umut
dc.contributor.advisorRas, Gabrielle
dc.contributor.authorLammers, Christian
dc.description.abstractDuring this research, I trained the convolutional neural network called SqeeuezNet on a data set consisting of 343:503 frames. The goal was to identify what facial regions are most expressive for lie detection. However, due to a not well- t data set, none of the facial regions turn out to be signi cantly important for deception de- tection. This may also be caused by an over tted model. Apart from that, it does seem that the area surrounding the eyebrows and the eyes are most valuable for detect- ing deception with each having a p value of 0:184 and 0:194 respectively. Nonetheless, di erent literature sug- gests that there is quite high potential for arti cial neu- ral networks, especially convolutional neural networks, to be successful in detecting deception especially when using multiple modalities like auditory features.en_US
dc.embargo.typePermanent embargoen_US
dc.thesis.facultyFaculteit der Sociale Wetenschappenen_US
dc.thesis.specialisationBachelor Artificial Intelligenceen_US
dc.thesis.studyprogrammeArtificial Intelligenceen_US
dc.titleDeception Detection Using a Convolutional Neural Networken_US
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