Deception Detection Using Random Forest and KNN

Every person lies to try and escape or avoid some situation. Lying is considered to be a form of deceit and can have a very negative impact in today’s judicial world. Criminals, employees, shoplifters and many other people lie to get around the punishment. These must be detected so that innocent people are not put in adverse situations and also to catch real criminals. Conventional methods for detecting dishonesty entail the use of scientific methodologies to examine physiological signs, transcripts, as well as visual and audio data. Nevertheless, there has been a dearth of exploration into the utilization of modalities like EEG data. Despite years of research, scientists have consistently demonstrated that people’s ability to discern deception is no better than random chance. Precision in detecting deception holds paramount importance for law enforcement authorities. In the proposed model, brain waves and audio cues will be combined to enhance the existing deception detection. Existing datasets like ‘Bag-of-Lies’ will be utilized. This system is poised to make a substantial contribution to the Police Department, Criminal Investigation Department, and Judicial System by reducing the burden on law enforcement officers and aiding in the accurate identification of the actual perpetrator.