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

2021-02-03 07:45:04
Wolfgang Fuhl
arXiv_CV
       
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. this https URL

Abstract (translated)

URL

https://arxiv.org/abs/2102.01921

PDF

https://arxiv.org/pdf/2102.01921.pdf

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