  This manuscript presents GazeBase, a large-scale longitudinal dataset
containing 12,334 monocular eye-movement recordings captured from 322
college-aged subjects. Subjects completed a battery of seven tasks in two
contiguous sessions during each round of recording, including a - 1) fixation
task, 2) horizontal saccade task, 3) random oblique saccade task, 4) reading
task, 5/6) free viewing of cinematic video task, and 7) gaze-driven gaming
task. A total of nine rounds of recording were conducted over a 37 month
period, with subjects in each subsequent round recruited exclusively from the
prior round. All data was collected using an EyeLink 1000 eye tracker at a
1,000 Hz sampling rate, with a calibration and validation protocol performed
before each task to ensure data quality. Due to its large number of subjects
and longitudinal nature, GazeBase is well suited for exploring research
hypotheses in eye movement biometrics, along with other emerging applications
applying machine learning techniques to eye movement signal analysis.
