Detecting concealment of intent in transportation screening: A proof of concept

Judee K. Burgoon, Douglas P. Twitchell, Matthew L. Jensen, Thomas O. Meservy, Mark Adkins, John Kruse, Amit V. Deokar, Gabriel Tsechpenakis, Shan Lu, Dimitris N. Metaxas, Jay F. Nunamaker, Robert E. Younger

Research output: Contribution to journalArticlepeer-review

36 Scopus citations


Transportation and border security systems have a common goal: to allow law-abiding people to pass through security and detain those people who intend to harm. Understanding how intention is concealed and how it might be detected should help in attaining this goal. In this paper, we introduce a multidisciplinary theoretical model of intent concealment along with three verbal and nonverbal automated methods for detecting intent: message feature mining, speech act profiling, and kinesic analysis. This paper also reviews a program of empirical research supporting this model, including several previously published studies and the results of a proof-of-concept study. These studies support the model by showing that aspects of intent can be detected at a rate that is higher than chance. Finally, this paper discusses the implications of these findings in an airport-screening scenario.

Original languageEnglish (US)
Article number4773224
Pages (from-to)103-112
Number of pages10
JournalIEEE Transactions on Intelligent Transportation Systems
Issue number1
StatePublished - 2009


  • Concealment
  • Kinesic analysis
  • Message feature mining
  • Security
  • Speech act profiling

ASJC Scopus subject areas

  • Automotive Engineering
  • Mechanical Engineering
  • Computer Science Applications


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