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Every day, hundreds of thousands of people pass through airport security checkpoints, border crossing stations, or other security screening measures. Security professionals must sift through countless interactions and ferret out high-risk individuals who represent a danger to other citizens. During each interaction, the security professional must decide whether the individual is being forthright or deceptive. This task is difficult because of the limits of human vigilance and perception and the small percentage of individuals who actually harbor hostile intent. Our research initiative is based on a behavioral approach to deception detection. We attempted to build an automated system that can infer deception or truthfulness from a set of features extracted from head and hands movements in a video. A validated and reliable behaviorally based deception analysis system could potentially have great impacts in augmenting humans' abilities to assess credibility. An automated, unobtrusive system identifies behavioral patterns that indicate deception from nonverbal behavioral cues and classifies deception and truth more accurately than many humans.