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Reliable iris localization using Hough transform, histogram-bisection, and eccentricity
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AI
DOI：10.1016/j.sigpro.2012.07.033.png)
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Abstract
En 中文
The iris technology recognizes individuals from their iris texture with great precision. However, it does not perform well for the non-ideal data, where the eye image may contain non-ideal issues such as the off-axis eye image, blurring, non-uniform illumination, hair, glasses, etc. It is because of their iris localization algorithms, which are developed for the ideal data. In this paper, we propose a reliable iris localization algorithm. It includes localizing a coarse iris location in the eye image using the Hough transform and image statistics; localizing the pupillary boundary using a bi-valued adaptive threshold and the two-dimensional (2D) shape properties; localizing the limbic boundary by reusing the Hough accumulator and image statistics; and finally, regularizing these boundaries using a technique based on the Fourier series and radial gradients. The proposed technique is tested on the public iris databases: CASIA V1, CASIA-IrisV3-Lamp, CASIA-IrisV4-Thousand, IITD V1.0, MMU V1.0, and MMU (new) V2.0. Experimental results obtained on these databases show superiority of the proposed technique over some state of the art iris localization techniques. (C) 2012 Elsevier B.V. All rights reserved.
Keywords:
Iris recognition
Iris localization
Iris segmentation
Eccentricity
Histogram
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