Deception Detection using Facial Action Coding System in Videos

Abstract

Humans lie to each other on a regular basis in one form or another. To lie to someone is to share altered facts. Facts are important for decision making in every situation. Without unaltered facts, potentially harmful decisions can be made which can alter someone’s life in ways that may not want. This is why it is important to catch misinformation before any harm can be caused by them. Deception detection in videos has gained traction in recent times for its various real-life applications. When a person lies, they show facial expressions, also known as microexpressions so fast that they can be ignored by the untrained eye. Microexpressions can be used as a basis for creating a deception detection system. Facial Action Coding System is utilized as a way to encode and extract data from facial muscle movement during truthful and deceptive confessions. This data is used to train a deep learning model that utilizes long short-term memory (LSTM) to train and create the system. The real-life trial dataset is used to train and test the system provided one of the best facial only approaches to deception detection. Cross-data validation is also tested using the Real-life trial dataset, the Silesian Deception Database, and the Bag-of-lies Deception Database. Cross-data validation is something that has not yet been attempted by anyone else for a deception detection system which provides unique insight for the field. The results show that adding different datasets to train a neural network for the sake of creating a deception detection system worsens the accuracy of the system but the reasoning behind the poorer results actually encourages attempts at creating better datasets