Abstract
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.
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Notes
- 1.
“8 of History’s Most Destructive Lies” - https://bit.ly/3kufvOM.
- 2.
Simple/Bidirectional RNN architecture - https://bit.ly/32XRtWc.
- 3.
LSTM cell architecture - https://bit.ly/306K454.
- 4.
GRU cell architecture - https://bit.ly/3kN4eIX.
- 5.
“Hiding true emotions: micro-expressions in eyes retrospectively concealed by mouth movements” - https://www.nature.com/articles/srep22049.
- 6.
MinMaxScaler it is sensitive to outliers: https://bit.ly/30hsICz.
- 7.
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Acknowledgments
We would like to express our deep gratitude to Professor Emily Paige Lloyd who enables us to use Miami University deception detection database [17], which made the results of this research possible.
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Rodriguez-Meza, B., Vargas-Lopez-Lavalle, R., Ugarte, W. (2022). Recurrent Neural Networks for Deception Detection in Videos. In: Botto-Tobar, M., Montes León, S., Torres-Carrión, P., Zambrano Vizuete, M., Durakovic, B. (eds) Applied Technologies. ICAT 2021. Communications in Computer and Information Science, vol 1535. Springer, Cham. https://doi.org/10.1007/978-3-031-03884-6_29
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