  We present labelled pupils in the wild (LPW), a novel dataset of 66
high-quality, high-speed eye region videos for the development and evaluation
of pupil detection algorithms. The videos in our dataset were recorded from 22
participants in everyday locations at about 95 FPS using a state-of-the-art
dark-pupil head-mounted eye tracker. They cover people with different
ethnicities, a diverse set of everyday indoor and outdoor illumination
environments, as well as natural gaze direction distributions. The dataset also
includes participants wearing glasses, contact lenses, as well as make-up. We
benchmark five state-of-the-art pupil detection algorithms on our dataset with
respect to robustness and accuracy. We further study the influence of image
resolution, vision aids, as well as recording location (indoor, outdoor) on
pupil detection performance. Our evaluations provide valuable insights into the
general pupil detection problem and allow us to identify key challenges for
robust pupil detection on head-mounted eye trackers.
