GazeBase: A Large-Scale, Multi-Stimulus, Longitudinal Eye Movement Dataset
This dataset addresses a data scarcity problem for researchers in eye movement analysis, though it is incremental as it builds on existing data collection methods.
The authors tackled the lack of large-scale longitudinal eye movement data by presenting GazeBase, a dataset with 12,334 recordings from 322 subjects over 37 months, enabling research in eye movement biometrics and machine learning applications.
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.