  We propose a data-driven method for automatic deception detection in
real-life trial data using visual and verbal cues. Using OpenFace with facial
action unit recognition, we analyze the movement of facial features of the
witness when posed with questions and the acoustic patterns using OpenSmile. We
then perform a lexical analysis on the spoken words, emphasizing the use of
pauses and utterance breaks, feeding that to a Support Vector Machine to test
deceit or truth prediction. We then try out a method to incorporate
utterance-based fusion of visual and lexical analysis, using string based
matching.
