  Previous studies have shown that eye movement data recorded at 1000 Hz can be
used to authenticate individuals. This study explores the effectiveness of eye
movement-based biometrics (EMB) by utilizing data from an eye-tracking
(ET)-enabled virtual reality (VR) headset (GazeBaseVR) and compares it to the
performance using data from a high-end eye tracker (GazeBase) that has been
downsampled to 250 Hz. The research also aims to assess the biometric potential
of both binocular and monocular eye movement data. GazeBaseVR dataset achieves
an equal error rate (EER) of 1.67% and a false rejection rate (FRR) at 10^-4
false acceptance rate (FAR) of 22.73% in a binocular configuration. This study
underscores the biometric viability of data obtained from eye-tracking-enabled
VR headset.
