An AI-Powered Lie Detection System Using Facial Micro-Expressions

Deciding whether a person is telling the truth matters in security screening, forensic questioning, recruitment interviews, and fraud inquiries, yet the available instruments are weak.The polygraph demands attached sensors and a trained operator, and unaided human judges perform only marginally above chance.Micro-expressions, which are involuntary facial movements lasting roughly a twenty-fifth to a fifth of a second, give a contactless cue, but they pass too quickly for people to follow reliably.This paper describes the design and prototype of a video-based deception analysis system built around the Real-life Deception Detection Dataset of 121 courtroom clips.It joins facial-landmark geometry with learned visual features, follows how both change over time with a recurrent network that carries an attention layer, and returns a Truth/Lie probability together with an account of the frames and facial regions that shaped it.The design rests on a short review of six recent studies and on informal consultations with a recruiter and a police officer.The upload, analysis, and explained-output interface of the prototype is working, whereas quantitative validation (accuracy, precision, recall, and F1-score) is still to be carried out, so no performance claim is made.The contributions are a documented architecture, an explicit evaluation protocol, and a discussion of ethical limits that treats the tool as support for a human examiner and not as a verdict.