1000 Pupil Segmentations in a Second using Haar Like Features and Statistical Learning
This method offers a significant speed improvement for high-speed eye trackers and energy-saving mobile eye tracking, which is beneficial for researchers and developers in human-computer interaction and ophthalmology.
This paper introduces a new method for pupil segmentation that achieves high computational efficiency, enabling 1000 pupil segmentations per second. It combines Haar-like features with conditional pupil shape probabilities to address noise and fluctuating light conditions, and uses statistical learning for efficient training.
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