  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 accuracy of 57\% 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.
