Method to Detect Eye Position Noise from Video-Oculography when Detection of Pupil or Corneal Reflection Position Fails

Abdulin, Evgeny; Friedman, Lee S.; Komogortsev, Oleg V.

doi:10.48550/arxiv.1709.02700

Cited by 1 publication

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although subsets of this data have been utilized in prior work [10][11][12][13][14][15][16][17][18][19][20][21] , this recent dissemination is the first release of the entire set of gaze recordings and corresponding target locations for applicable stimuli. As the experimental parameters of the data collection were chosen to maximize the utility of the resulting data for biometric applications, GazeBase is well suited for supporting further investigation of emerging machine learning biometric techniques to the eye movement domain, such as metric learning 22 .…”

Section: Background and Summarymentioning

confidence: 99%

GazeBase: A Large-Scale, Multi-Stimulus, Longitudinal Eye Movement Dataset

Griffith¹,

Lohr²,

Abdulin³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

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.

show abstract