Unobtrusive Deception Detection

Aaron Elkins, Stefanos Zafeiriou, Maja Pantic, Judee Burgoon

    Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review


    In response to national security needs and human deception detection limitations paired with advances in sensor and computing technology research into automated deception detection has increased in recent years. These technologies rely on psychological and communication theories of deception to interpret when behavioral and physiological cues reveal deception. Despite this ever-present need, technology for detecting deception that is available to law enforcement or border guards is very limited. Based on deception theories, liars are predicted to exhibit both strategic and nonstrategic behavior. In order to develop algorithms and technology to detect and classify deception, these behaviors and physiology must be measured remotely. These measurements can be categorized by their theorized causes when lying and include arousal, negative affect, cognitive effort, behavioral control, memory, and strategic activity. One major challenge to deception detection is accounting for the variability introduced by human interviewers. Future research should focus more on behavior over the entire interaction and fusing multiple behavioral indicators of deception.
    Original languageUndefined
    Title of host publicationThe Oxford Handbook of Affective Computing
    EditorsRafael Calvo, Sidney D' Mello, Jonathan Gratch, Arvid Kappas
    Place of PublicationOxford, UK
    PublisherOxford University Press
    Number of pages10
    ISBN (Print)978-0-19-994223-7
    Publication statusPublished - Aug 2014

    Publication series

    PublisherOxford University Press


    • HMI-HF: Human Factors
    • theoretical
    • EWI-25772
    • physiological cues
    • IR-102926
    • Deception
    • Nonverbal Behavior
    • strategic and nonstrategic behavior
    • automated deception detection

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