• DOI:10.1145/3290605.3300451
• Corpus ID: 140213401

Detecting Personality Traits Using Eye-Tracking Data

@article{Berkovsky2019DetectingPT,
  title={Detecting Personality Traits Using Eye-Tracking Data},
  author={Shlomo Berkovsky and Ronnie Taib and Irena Koprinska and Eileen Wang and Yucheng Zeng and Jingjie Li and Sabina Kleitman},
  journal={Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems},
  year={2019}
}

• S. Berkovsky, R. Taib, +4 authors Sabina Kleitman
• Published 2 May 2019
• Psychology
• Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems

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.