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dc.contributor.authorRodriguez-Meza, Bryan
dc.contributor.authorVargas-Lopez-Lavalle, Renzo
dc.contributor.authorUgarte, Willy
dc.date.accessioned2022-05-06T17:48:32Z
dc.date.available2022-05-06T17:48:32Z
dc.date.issued2022-01-01
dc.identifier.issn18650929
dc.identifier.doi10.1007/978-3-031-03884-6_29
dc.identifier.urihttp://hdl.handle.net/10757/659825
dc.description.abstractDeception detection has always been of subject of interest. After all, determining if a person is telling the truth or not could be detrimental in many real-world cases. Current methods to discern deceptions require expensive equipment that need specialists to read and interpret them. In this article, we carry out an exhaustive comparison between 9 different facial landmark recognition based recurrent deep learning models trained on a recent man-made database used to determine lies, comparing them by accuracy and AUC. We also propose two new metrics that represent the validity of each prediction. The results of a 5-fold cross validation show that out of all the tested models, the Stacked GRU neural model has the highest AUC of.9853 and the highest accuracy of 93.69% between the trained models. Then, a comparison is done between other machine and deep learning methods and our proposed Stacked GRU architecture where the latter surpasses them in the AUC metric. These results indicate that we are not that far away from a future where deception detection could be accessible throughout computers or smart devices.es_PE
dc.formatapplication/htmles_PE
dc.language.isoenges_PE
dc.publisherSpringer Science and Business Media Deutschland GmbHes_PE
dc.relation.urlhttps://link.springer.com/chapter/10.1007/978-3-031-03884-6_29es_PE
dc.rightsinfo:eu-repo/semantics/embargoedAccesses_PE
dc.subjectDeception detectiones_PE
dc.subjectDeep learninges_PE
dc.subjectFacial landmarks recognitiones_PE
dc.subjectRecurrent neural networkses_PE
dc.subjectVideo databasees_PE
dc.titleRecurrent neural networks for deception detection in videoses_PE
dc.typeinfo:eu-repo/semantics/articlees_PE
dc.identifier.eissn18650937
dc.identifier.journalCommunications in Computer and Information Sciencees_PE
dc.description.peerreviewRevisión por pareses_PE
dc.identifier.eid2-s2.0-85128491751
dc.identifier.scopusidSCOPUS_ID:85128491751
dc.source.journaltitleCommunications in Computer and Information Science
dc.source.volume1535 CCIS
dc.source.beginpage397
dc.source.endpage411


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