Deception detection through automatic, unobtrusive analysis of nonverbal behavior
Deception detection through automatic, unobtrusive analysis of nonverbal behavior
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DOI：
10.1109/mis.2005.85
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发表时间：
2005-09-01
影响因子：
6.4
通讯作者：
Metaxas, DN
中科院分区：
文献类型：
--
作者：
Meservy, TO;Jensen, ML;Metaxas, DN
每天，都有数十万人通过机场安全检查站、过境站或其他安全检查措施。安全专业人员必须筛选无数的互动，找出对其他公民构成危险的高风险个人。在每次互动中，安全专业人员必须确定此人是直率还是欺骗。这项任务很困难，因为人类的警惕性和感知力有限，而且实际上怀有敌对意图的人所占比例很小。我们的研究计划基于欺骗检测的行为方法。我们试图建立一个自动化系统，可以根据从视频中头部和手部运动中提取的一组特征来推断欺骗或真实性。一个经过验证且可靠的基于行为的欺骗分析系统可能会对增强人类评估可信度的能力产生巨大影响。自动化、不显眼的系统可以从非语言行为线索中识别出表明欺骗的行为模式，并比许多人类更准确地对欺骗和真相进行分类。
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.