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GazeBase, a large-scale, multi-stimulus, longitudinal eye movement dataset.

Henry Griffith1, Dillon Lohr2, Evgeny Abdulin2

  • 1Texas State University, Department of Computer Science, San Marcos, TX, 78666, USA. h_169@txstate.edu.

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Summary

This study introduces GazeBase, a large longitudinal dataset of eye-movement recordings from 322 participants. This dataset supports research in eye movement biometrics and machine learning applications.

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Area of Science:

  • Ophthalmology
  • Computer Science
  • Biometrics

Background:

  • Eye movement analysis is crucial for understanding visual attention and cognitive processes.
  • Large-scale, longitudinal datasets are needed to develop robust eye-tracking models.
  • Existing datasets may lack the diversity or duration required for comprehensive research.

Purpose of the Study:

  • To introduce GazeBase, a novel, large-scale longitudinal dataset of monocular eye-movement recordings.
  • To provide a valuable resource for research in eye movement biometrics and machine learning.
  • To facilitate the development of advanced algorithms for eye-tracking data analysis.

Main Methods:

  • Collected 12,334 monocular eye-movement recordings from 322 college-aged participants over 37 months.
  • Utilized an EyeLink 1000 eye tracker with a 1,000 Hz sampling rate.
  • Included a battery of seven diverse tasks (fixation, saccades, reading, free viewing, gaming) across nine recording rounds.

Main Results:

  • Established GazeBase, a comprehensive dataset with extensive longitudinal eye-tracking data.
  • Ensured data quality through rigorous calibration and validation protocols before each task.
  • Provided classification labels and pupil area data for a subset of the recordings.

Conclusions:

  • GazeBase is a unique resource for studying eye movement patterns and individual differences over time.
  • The dataset's scale and longitudinal nature are ideal for developing and validating machine learning models for eye movement biometrics.
  • Facilitates future research in areas such as cognitive state monitoring and human-computer interaction.