GazeBaseVR, a large-scale, longitudinal, binocular eye-tracking dataset collected in virtual reality

PubDate: Oct 2022

Teams: Texas State University;

Writers: Dillon Lohr, Samantha Aziz, Lee Friedman, Oleg V Komogortsev

PDF: GazeBaseVR, a large-scale, longitudinal, binocular eye-tracking dataset collected in virtual reality

Abstract

We present GazeBaseVR, a large-scale, longitudinal, binocular eye-tracking (ET) dataset collected at 250 Hz with an ET-enabled virtual-reality (VR) headset. GazeBaseVR comprises 5,020 binocular recordings from a diverse population of 407 college-aged participants. Participants were recorded up to six times each over a 26-month period, each time performing a series of five different ET tasks: (1) a vergence task, (2) a horizontal smooth pursuit task, (3) a video-viewing task, (4) a self-paced reading task, and (5) a random oblique saccade task. Many of these participants have also been recorded for two previously published datasets with different ET devices, and some participants were recorded before and after COVID-19 infection and recovery. GazeBaseVR is suitable for a wide range of research on ET data in VR devices, especially eye movement biometrics due to its large population and longitudinal nature. In addition to ET data, additional participant details are provided to enable further research on topics such as fairness.