research-article

Detecting Personality Traits Using Eye-Tracking Data

Authors:

Shlomo Berkovsky,

Irena Koprinska,

Jingjie Li, and

Sabina KleitmanAuthors Info & Claims

CHI '19: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems

May 2019

Paper No.: 221, Pages 1 - 12

https://doi.org/10.1145/3290605.3300451

Published: 02 May 2019 Publication History

Abstract

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.

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Index Terms

Detecting Personality Traits Using Eye-Tracking Data
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Empirical studies in HCI

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cover image ACM Conferences

CHI '19: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems

May 2019

9077 pages

ISBN:9781450359702

DOI:10.1145/3290605

General Chairs:
Stephen Brewster
University of Glasgow, Scotland, UK
,
Geraldine Fitzpatrick
TU Wien, Austria
,
Program Chairs:
Anna Cox
University College London, UK
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Vassilis Kostakos
University of Melbourne, Australia

Copyright © 2019 ACM.

© 2019 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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https://doi.org/10.3390/ai5020034
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