info:eu-repo/semantics/article
Recurrent neural networks for deception detection in videos
Fecha
2022-01-01Registro en:
18650929
10.1007/978-3-031-03884-6_29
18650937
Communications in Computer and Information Science
2-s2.0-85128491751
SCOPUS_ID:85128491751
Autor
Rodriguez-Meza, Bryan
Vargas-Lopez-Lavalle, Renzo
Ugarte, Willy
Institución
Resumen
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.