1000 Pupil Segmentations in a Second using Haar Like Features and Statistical Learning

Wolfgang Fuhl

Abstract:

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