Unobtrusive Deception Detection

Elkins, Aaron; Zafeiriou, Stefanos; Pantić, Maja; Burgoon, Judee K.

doi:10.1093/oxfordhb/9780199942237.013.037

Cited by 3 publications

(1 citation statement)

References 74 publications

(97 reference statements)

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“…When applied to deception detection, these limitations have forced researchers to focus on constrained recording conditions (Elkins et al, 2014 ). For example, Lu et al ( 2005 ) detected the hands of a subject using skin color and blob analysis.…”

Section: Introductionmentioning

confidence: 99%

Mining Bodily Cues to Deception

Poppe,

van der Zee,

Taylor

et al. 2024

J Nonverbal Behav

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A significant body of research has investigated potential correlates of deception and bodily behavior. The vast majority of these studies consider discrete, subjectively coded bodily movements such as specific hand or head gestures. Such studies fail to consider quantitative aspects of body movement such as the precise movement direction, magnitude and timing. In this paper, we employ an innovative data mining approach to systematically study bodily correlates of deception. We re-analyze motion capture data from a previously published deception study, and experiment with different data coding options. We report how deception detection rates are affected by variables such as body part, the coding of the pose and movement, the length of the observation, and the amount of measurement noise. Our results demonstrate the feasibility of a data mining approach, with detection rates above 65%, significantly outperforming human judgement (52.80%). Owing to the systematic analysis, our analyses allow for an understanding of the importance of various coding factor. Moreover, we can reconcile seemingly discrepant findings in previous research. Our approach highlights the merits of data-driven research to support the validation and development of deception theory.

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Section: Introductionmentioning

confidence: 99%

Mining Bodily Cues to Deception

Poppe,

van der Zee,

Taylor

et al. 2024

J Nonverbal Behav

View full text Add to dashboard Cite

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Verbal and Non-Verbal Features in Deception Detection Systems

Abed

Hashim

Jalal

2020

2020 3rd International Conference on Engineering Technology and Its Applications (IICETA)

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Automated Deception Detection Systems, a Review

Shaimaa

Hashim

Jalal

2021

eijs

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Humans use deception daily since it can significantly affect their life and provide a getaway solution for any undesired situation. Deception is either related to low-stakes (e.g. innocuous) or high-stakes (e.g. with harmful situations). Deception investigation importance has increased, and it became a critical issue over the years with the increase of security levels around the globe. Technology has made remarkable achievements in many human life fields, including deception detection. Automated deception detection systems (DDSs) are widely used in different fields, especially for security purposes. The DDS is comprised of multiple stages, each of which should be built/trained to perform intelligently so that the whole system can give the right decision of whether the involved person is telling the truth or not. Thus, different artificial intelligent (AI) algorithms have been utilized by the researchers over the past years. In addition, there are different cues for DDS that have been considered for the previous works, which are either related to verbal or non-verbal cues. This paper presents a review on the basic methods and the used deception detection techniques for the recent 10 years, that were studied and performed in the field of DDS, with a comparison of the deception detection accuracy reached and the number of participants used for system training.

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Unobtrusive Deception Detection

Cited by 3 publications

References 74 publications

Mining Bodily Cues to Deception

Mining Bodily Cues to Deception

Verbal and Non-Verbal Features in Deception Detection Systems

Automated Deception Detection Systems, a Review

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