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Issue Date
2022-01-01Keywords
Deception detectionDeep learning
Facial landmarks recognition
Recurrent neural networks
Video database
Metadata
Show full item recordJournal
Communications in Computer and Information ScienceDOI
10.1007/978-3-031-03884-6_29Additional Links
https://link.springer.com/chapter/10.1007/978-3-031-03884-6_29Abstract
Deception 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.Type
info:eu-repo/semantics/articleRights
info:eu-repo/semantics/embargoedAccessLanguage
engISSN
18650929EISSN
18650937ae974a485f413a2113503eed53cd6c53
10.1007/978-3-031-03884-6_29
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