  In this paper we present a new approach for pupil segmentation. It can be
computed and trained very efficiently, making it ideal for online use for high
speed eye trackers as well as for energy saving pupil detection in mobile eye
tracking. The approach is inspired by the BORE and CBF algorithms and
generalizes the binary comparison by Haar features. Since these features are
intrinsically very susceptible to noise and fluctuating light conditions, we
combine them with conditional pupil shape probabilities. In addition, we also
rank each feature according to its importance in determining the pupil shape.
Another advantage of our method is the use of statistical learning, which is
very efficient and can even be used online.
https://atreus.informatik.uni-tuebingen.de/seafile/d/8e2ab8c3fdd444e1a135/?p=%2FStatsPupil&mode=list
