Adapting Starburst for Elliptical Iris Segmentation
- 1 January 2008
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 1-7
Abstract
Fitting an ellipse to the iris boundaries accounts for the projective distortions present in off-axis images of the eye and provides the contour fitting necessary for the dimensionless mapping used in leading iris recognition algorithms. Previous iris segmentation efforts have either focused on fitting circles to pupillary and limbic boundaries or assigning labels to image pixels. This paper approaches the iris segmentation problem by adapting the starburst algorithm to locate pupillary and limbic feature pixels used to fit a pair of ellipses. The approach is evaluated by comparing the fits to ground truth. Two metrics are used in the evaluation, the first based on the algebraic distance between ellipses, the second based on ellipse chamfer images. Results are compared to segmentations produced by ND_IRIS over randomly selected images from the iris challenge evaluation database. Statistical evidence shows significant improvement of starburst's elliptical fits over the circular fits on which ND_IRIS relies.Keywords
This publication has 15 references indexed in Scilit:
- Non-ideal iris segmentation using graph cuts2008
- Limbus/pupil switching for wearable eye tracking under variable lighting conditions2008
- Experiments with an Improved Iris Segmentation Algorithm2006
- Improving the Performance of Iris Recogniton System Using Eyelids and Eyelashes Detection and Iris Image Enhancement2006
- Iris segmentation methodology for non-cooperative recognitionIEE Proceedings - Vision, Image, and Signal Processing, 2006
- Iris pattern extraction using bit planes and standard deviations2005
- Direct least square fitting of ellipsesIEEE Transactions on Pattern Analysis and Machine Intelligence, 1999
- High confidence visual recognition of persons by a test of statistical independenceIEEE Transactions on Pattern Analysis and Machine Intelligence, 1993
- Using dynamic programming for solving variational problems in visionIEEE Transactions on Pattern Analysis and Machine Intelligence, 1990
- Snakes: Active contour modelsInternational Journal of Computer Vision, 1988