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Eye-Tracking Feature Extraction for Biometric Machine Learning.

Jia Zheng Lim1, James Mountstephens2, Jason Teo2

  • 1Evolutionary Computing Laboratory, Faculty of Computing and Informatics, Universiti Malaysia Sabah, Kota Kinabalu, Malaysia.

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Summary

This review identifies key machine learning features from eye-tracking data for classification tasks. Fixations are the most frequently utilized feature in studies analyzing eye movements.

Keywords:
biometric machine learningclassificationeye-trackingfeature extractionfixation

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Area of Science:

  • Human-Computer Interaction
  • Biometrics
  • Machine Learning

Background:

  • Eye tracking technology captures eye movements and positions, offering insights into human behavior and computer interaction.
  • Eye-tracking data enables passive biometric classification, including emotion prediction.
  • This review focuses on machine learning features derived from eye-tracking data for classification.

Purpose of the Study:

  • To systematically review machine learning features obtainable from eye-tracking data for classification.
  • To identify the most prevalent features used in eye-tracking classification studies.

Main Methods:

  • A systematic literature review (SLR) was conducted from 2016 to the present.
  • Four databases (IEEE Xplore, ACM Digital Library, ScienceDirect, Google Scholar) were searched.
  • The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology guided article selection.

Main Results:

  • An initial search yielded 420 articles.
  • 37 relevant articles were selected for qualitative synthesis based on the methodology.
  • Key features identified include pupil size, saccades, fixations, velocity, blinks, pupil position, electrooculogram (EOG), and gaze point.

Conclusions:

  • Eye-tracking data provides a rich set of features for machine learning classification.
  • Fixations emerged as the most commonly employed feature in the reviewed studies.
  • Further research can leverage these features for advanced biometric and behavioral analysis.