Comparing a Perceptual and an Automated Vision-Based Method for Lie Detection in Younger Children

Mariana Vieira Da Fonseca Serras Pereira, Rein Cozijn, Eric Postma, Suleman Shahid, Marc Swerts

Research output: Contribution to journalArticleScientificpeer-review

2 Citations (Scopus)
122 Downloads (Pure)


The present study investigates how easily it can be detected whether a child is being truthful or not in a game situation, and it explores the cue validity of bodily movements for such type of classification. To achieve this, we introduce an innovative methodology – the combination of perception studies (in which eye-tracking technology is being used) and automated movement analysis. Film fragments from truthful and deceptive children were shown to human judges who were given the task to decide whether the recorded child was being truthful or not. Results reveal that judges are able to accurately distinguish truthful clips from lying clips in both perception studies. Even though the automated movement analysis for overall and specific body regions did not yield significant results between the experimental conditions, we did find a positive correlation between the amount of movement in a child and the perception of lies, i.e., the more movement the children exhibited during a clip, the higher the chance that the clip was perceived as a lie. The eye-tracking study revealed that, even when there is movement happening in different body regions, judges tend to focus their attention mainly on the face region. This is the first study that compares a perceptual and an automated method for the detection of deceptive behavior in children whose data have been elicited through an ecologically valid paradigm.
Original languageEnglish
Article number1936
Number of pages12
JournalFrontiers in Psychology
Publication statusPublished - 12 Dec 2016


  • children
  • Eye tracking
  • deception detection
  • video analysis
  • Nonverbal Communication
  • motion analysis


Dive into the research topics of 'Comparing a Perceptual and an Automated Vision-Based Method for Lie Detection in Younger Children'. Together they form a unique fingerprint.

Cite this