Lie detection is considered a concern for everyone in their day to day life given its impact on human interactions. Hence, people are normally not only pay attention to  what their interlocutors are saying but also try to inspect their visual appearances, including faces, to  find any signs that indicate whether the person is telling the truth or not. Unfortunately to date, the automatic lie detection , which may help us to understand this lying characteristics  are still fairly limited . Mainly due to lack of a lie dataset and corresponding evaluations . In this work, we have collected a dataset that contains annotated images and 3D information of different participants faces during a card game that incentivise the lying . Using our collected dataset, we evaluated several types of machine learning based lie detector through generalize, personal and cross lie lie experiments. In these experiments, we showed the superiority of deep learning based model in recognizing the lie with best accuracyof 57\\%DIF < for generalized task and 63\\% when dealing with a single participant. Finally, we also highlight the limitation of the deep learning based lie detector when dealing with different types of lie tasks.\end{abstract} %DIF > for the generalization task and 63\\% when dealing with a single participant. Finally, we also highlight the limitation of the deep learning based lie detector when dealing with cross-domain lie detection tasks.
Lie detection is considered a concern for everyone in their day to day life given its impact on human interactions. Thus, people normally pay attention to both what their interlocutors are saying and also to their visual appearances, including faces, to try to find any signs that indicate whether the person is telling the truth or not. While automatic lie detection may help us to understand this lying characteristics , current systems are still fairly limited , partly due to lack of adequate datasets to evaluate their performance in realistic scenarios . In this work, we have collected an annotated dataset of facial images, comprising both 2D and 3D information of several participants during a card game that encourages players to lie . Using our collected dataset, We evaluated several types of machine learning-based lie detectors in terms of their generalization, person-specific and cross-domain experiments. Our results show that models based on deep learning achieve the best accuracy, reaching up to 57\\%DIF < for generalized task and 63\\% when dealing with a single participant. Finally, we also highlight the limitation of the deep learning based lie detector when dealing with different types of lie tasks.\end{abstract} %DIF > for the generalization task and 63\\% when dealing with a single participant. Finally, we also highlight the limitation of the deep learning based lie detector when dealing with cross-domain lie detection tasks.
R
Hence, people are normally not only
Thus, people normally
A
both
R
but also try to inspect
and also to
A
try to
R
Unfortunately to date, the
While
D
, which
A
, current systems
R
. Mainly
, partly
R
a lie dataset and corresponding evaluations
adequate datasets to evaluate their performance in realistic scenarios
R
a dataset that contains annotated images
an annotated dataset of facial images, comprising both 2D
R
different participants faces
several participants
R
incentivise the lying
encourages players to lie
R
we
We
R
learning based lie detector through generalize, personal and cross lie lie experiments. In these experiments, we showed the superiority of deep learning based model in recognizing the lie with best accuracyof
learning-based lie detectors in terms of their generalization, person-specific and cross-domain experiments. Our results show that models based on deep learning achieve the best accuracy, reaching up to