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

TL;DR

This paper introduces a fast, efficient pupil segmentation method using Haar-like features combined with statistical learning, suitable for real-time and energy-efficient eye tracking applications.

Contribution

It generalizes binary comparison algorithms with Haar features and incorporates statistical learning for online, robust pupil detection under noisy and variable lighting conditions.

Findings

Achieves high-speed pupil segmentation in under a second

Effective under noisy and fluctuating light conditions

Suitable for online, energy-efficient eye tracking

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. https://atreus.informatik.uni-tuebingen.de/seafile/d/8e2ab8c3fdd444e1a135/?p=%2FStatsPupil&mode=list