Detecting personality traits using eye-tracking data

Shlomo Berkovsky*, Ronnie Taib, Irena Koprinska, Eileen Wang, Yucheng Zeng, Jingjie Li, Sabina Kleitman

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

59 Citations (Scopus)


Personality is an established domain of research in psychology, and individual differences in various traits are linked to a variety of real-life outcomes and behaviours. Personality detection is an intricate task that typically requires humans to fill out lengthy questionnaires assessing specific personality traits. The outcomes of this, however, may be unreliable or biased if the respondents do not fully understand or are not willing to honestly answer the questions. To this end, we propose a framework for objective personality detection that leverages humans' physiological responses to external stimuli. We exemplify and evaluate the framework in a case study, where we expose subjects to affective image and video stimuli, and capture their physiological responses using a commercial-grade eye-tracking sensor. These responses are then processed and fed into a classifier capable of accurately predicting a range of personality traits. Our work yields notably high predictive accuracy, suggesting the applicability of the proposed framework for robust personality detection.

Original languageEnglish
Title of host publicationCHI 2019 Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems
Place of PublicationNew York
PublisherAssociation for Computing Machinery (ACM)
Number of pages12
ISBN (Electronic)9781450359702
Publication statusPublished - 2019
EventCHI Conference on Human Factors in Computing Systems (CHI) - Glasgow
Duration: 4 May 20199 May 2019


ConferenceCHI Conference on Human Factors in Computing Systems (CHI)


  • Personality detection
  • framework
  • eye tracking
  • field study


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