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Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/15150

Title: An Efficient Iris Segmentation Approach
Authors: Abdu Gomai
Ali El Zaart
Hassan Mathkour
Keywords: Iris Segmentation; Pupil Detection; Minimum and Mean Intensity of Pupil; Iris Recognition System; Image Processing Tools.
Issue Date: 2010
Abstract: Iris recognition system became a reliable system for authentication and verification tasks. It consists of five stages: image acquisition, iris segmentation, iris normalization, feature encoding, and feature matching. Iris segmentation stage is one of the most important stages that plays an essential role to locate the iris efficiently and accurately. In this paper, we present a new approach for iris segmentation using image processing technique. This approach is composed of four main parts. (1) Eliminating reflections of light on the eye image based on inverting the color of the grayscale image, filling holes in the intensity image, and inverting the color of the intensity image to get the original grayscale image without any reflections. (2) Pupil boundary detection based on dividing an eye image to nine sub-images and finding the minimum value of the mean intensity for each sub-image to get a suitable threshold value of pupil. (3) Enhancing the contrast of outer iris boundary using exponential operator to have sharp variation. (4) Outer iris boundary localization based on applying a gray threshold and morphological operations on the rectangular part of an eye image including the pupil and the outer boundaries of iris to find the small radius of outer iris boundary from the center of pupil. The proposed approach has been tested on CASIA v1.0 iris image database and other collected iris image database. The experimental results show that the approach is able to detect pupil and outer iris boundary with high accuracy results approximately 100% and reduce time consuming.
URI: http://hdl.handle.net/123456789/15150
Appears in Collections:College of Computer and Information Sciences

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