  With advancements in hardware, high-quality HMD devices are being developed
by numerous companies, driving increased consumer interest in AR, VR, and MR
applications. In this work, we present a new dataset, called VRBiom, of
periocular videos acquired using a Virtual Reality headset. The VRBiom,
targeted at biometric applications, consists of 900 short videos acquired from
25 individuals recorded in the NIR spectrum. These 10s long videos have been
captured using the internal tracking cameras of Meta Quest Pro at 72 FPS. To
encompass real-world variations, the dataset includes recordings under three
gaze conditions: steady, moving, and partially closed eyes. We have also
ensured an equal split of recordings without and with glasses to facilitate the
analysis of eye-wear. These videos, characterized by non-frontal views of the
eye and relatively low spatial resolutions (400 x 400), can be instrumental in
advancing state-of-the-art research across various biometric applications. The
VRBiom dataset can be utilized to evaluate, train, or adapt models for
biometric use-cases such as iris and/or periocular recognition and associated
sub-tasks such as detection and semantic segmentation.
  In addition to data from real individuals, we have included around 1100 PA
constructed from 92 PA instruments. These PAIs fall into six categories
constructed through combinations of print attacks (real and synthetic
identities), fake 3D eyeballs, plastic eyes, and various types of masks and
mannequins. These PA videos, combined with genuine (bona-fide) data, can be
utilized to address concerns related to spoofing, which is a significant threat
if these devices are to be used for authentication.
  The VRBiom dataset is publicly available for research purposes related to
biometric applications only.
