  Facts are important in decision making in every situation, which is why it is
important to catch deceptive information before they are accepted as facts.
Deception detection in videos has gained traction in recent times for its
various real-life application. In our approach, we extract facial action units
using the facial action coding system which we use as parameters for training a
deep learning model. We specifically use long short-term memory (LSTM) which we
trained using the real-life trial dataset and it provided one of the best
facial only approaches to deception detection. We also tested cross-dataset
validation using the Real-life trial dataset, the Silesian Deception Dataset,
and the Bag-of-lies Deception Dataset which has not yet been attempted by
anyone else for a deception detection system. We tested and compared all
datasets amongst each other individually and collectively using the same deep
learning training model. The results show that adding different datasets for
training worsen the accuracy of the model. One of the primary reasons is that
the nature of these datasets vastly differs from one another.
