NDLI: Robust iris segmentation based on learned boundary detectors

Content Provider	IEEE Xplore Digital Library
Author	Haiqing Li Zhenan Sun Tieniu Tan
Copyright Year	2012
Description	Author affiliation: National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, P.R. China, 100190 (Haiqing Li; Zhenan Sun; Tieniu Tan)
Abstract	Iris segmentation aims to isolate the valid iris texture regions useful for personal identification from the background of an iris image. Most state-of-the-art iris segmentation methods are based on edge information. However, generic edge detection methods may generate a large number of noisy edge points which can mislead iris localization. Therefore a robust iris segmentation method based on specific edge detectors is proposed in this paper. Firstly, a set of visual features including intensity, gradient, texture and structure information is used to characterize the edge points on iris boundaries. Secondly, AdaBoost is employed to learn six class-specific boundary detectors for localization of left/right pupillary boundaries, left/right limbic boundaries and upper/lower eyelids respectively. Thirdly, inner and outer boundaries of the iris ring are localized using weighted Hough transforms based on the output of the corresponding detectors. Finally, the edge points on the eyelids are detected and fitted as parabolas by robust least squares fitting. Extensive experiments on the challenging CASIA-Iris-Thousand iris image database demonstrate the effectiveness of the proposed iris segmentation method.
Starting Page	317
Ending Page	322
File Size	2761896
Page Count	6
File Format	PDF
ISBN	9781467303965
e-ISBN	9781467303972
DOI	10.1109/ICB.2012.6199826
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-03-29
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Iris Image segmentation Image edge detection Detectors Transforms Iris recognition
Content Type	Text
Resource Type	Article

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