Deception detection through automatic, unobtrusive analysis of nonverbal behavior

Thomas O. Meservy, Matthew L. Jensen, John Kruse, Judee K. Burgoon, Jay F. Nunamaker, Douglas P. Twitchell, Gabriel Tsechpenakis, Dimitris N. Metaxas

Research output: Contribution to journalReview articlepeer-review

71 Scopus citations

Abstract

An approach for deception detection through automatic, unobtrusive analysis of nonverbal behavior was described. An automated unobtrusive system identifies behavioral patterns that indicate deception from nonverbal behavioral cues and classifies deception and truth more accurately than many humans. Automated systems can draw upon a wide variety of potential behavioral indicators of deception. It is expected that the automated systems might become reliable enough to replace humans in certain circumstances, thus allowing a redistribution of human assets.

Original languageEnglish (US)
Pages (from-to)36-43
Number of pages8
JournalIEEE Intelligent Systems
Volume20
Issue number5
DOIs
StatePublished - Sep 1 2005

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Artificial Intelligence

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