Analysing error of fit functions for ellipses
Pattern Recognition Letters
Direct Least Square Fitting of Ellipses
IEEE Transactions on Pattern Analysis and Machine Intelligence
A linear-time component-labeling algorithm using contour tracing technique
Computer Vision and Image Understanding
In the Eye of the Beholder: A Survey of Models for Eyes and Gaze
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evaluation of a low-cost open-source gaze tracker
Proceedings of the 2010 Symposium on Eye-Tracking Research & Applications
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
A miniature head-mounted camera for measuring eye closure
Proceedings of the 27th Conference on Image and Vision Computing New Zealand
A development and evaluation platform for non-tactile power wheelchair controls
Proceedings of the 6th International Conference on PErvasive Technologies Related to Assistive Environments
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Robust, accurate, real-time pupil tracking is a key component for online gaze estimation. On head-mounted eye trackers, existing algorithms that rely on circular pupils or contiguous pupil regions fail to detect or accurately track the pupil. This is because the pupil ellipse is often highly eccentric and partially occluded by eyelashes. We present a novel, real-time dark-pupil tracking algorithm that is robust under such conditions. Our approach uses a Haar-like feature detector to roughly estimate the pupil location, performs a k-means segmentation on the surrounding region to refine the pupil centre, and fits an ellipse to the pupil using a novel image-aware Random Sample Concensus (RANSAC) ellipse fitting. We compare our approach against existing real-time pupil tracking implementations, using a set of manually labelled infra-red dark-pupil eye images. We show that our technique has a higher pupil detection rate and greater pupil tracking accuracy.