NDLI: Iris Localization with Dual Coarse-to-fine Strategy

Content Provider	IEEE Xplore Digital Library
Author	Xinhua Feng Chi Fang Xiaoqing Ding Youshou Wu
Copyright Year	2006
Description	Author affiliation: Dept. of Electron. Eng., Tsinghua Univ., Beijing (Xinhua Feng; Chi Fang; Xiaoqing Ding; Youshou Wu)
Abstract	Iris-based personal recognition is highly dependent on the accurate iris localization. In this paper, an effective and efficient iris localization algorithm is proposed to overcome the drawback of the traditional localization methods which are time-consuming and sensitive to the occlusion caused by eyelids and eyelashes. The coarse-to-fine strategy is deployed in both the inner boundary localization and the outer boundary localization. In the coarse localization of the inner boundary, the lower contour of the pupil is introduced to estimate the parameters of the pupil since it is stable even when the iris image is seriously occluded. While in the coarse localization of the outer boundary, the average intensity signals on both sides of the pupil are utilized to estimate the parameters of the sclera after the fine localization of the inner boundary. In the fine stage, the Hough transform is adopted to localize both boundaries precisely with the gradient information. Experimental results indicate that the proposed method is more effective and efficient
Sponsorship	IEEE CPS
Starting Page	553
Ending Page	556
File Size	310128
Page Count	4
File Format	PDF
ISBN	0769525210
ISSN	10514651
DOI	10.1109/ICPR.2006.725
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-08-20
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Parameter estimation Iris recognition Eyelids Eyelashes Information security Stability Feature extraction Integral equations Image edge detection Computational efficiency
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition

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