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Using artificial neural networks and feature saliency to identify iris measurements that contain the most discriminatory information for iris segmentation

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Using artificial neural networks and feature saliency to identify iris measurements that contain the most discriminatory information for iris segmentation

Content Provider	IEEE Xplore Digital Library
Author	Broussard, R.P. Ives, R.W.
Copyright Year	2009
Description	Author affiliation: Weapons and Systems Engineering Department, United States Naval Academy, Annapolis, MD 21402 USA (Broussard, R.P.) \|\| Electrical and Computer Engineering Department, United States Naval Academy, Annapolis, MD 21402 USA (Ives, R.W.)
Abstract	One of the basic challenges to robust iris recognition is iris segmentation. To represent the iris, some researchers fit circles, ellipses or active contours to the boundary pixels of the segmented iris. In order to get an accurate fit, the iris boundary must first be accurately identified. Some segmentation methods operate on a pre-processed gray-scaled image, while others use a thresholded binary edge image. The Hough transform is a popular method used to search for circular or elliptical patterns within the image. Many irises are slightly elliptical, and suffer from eyelid/eyelash occlusion, specular reflections and often the pupil and iris centers are not co-located. Each of these issues can cause a segmentation error. This research uses of a feature saliency algorithm to identify which measurements, used in common iris segmentation methods, jointly contain the most discriminatory information for identify the iris boundary. Once this feature set is identified, an artificial neural network is used to near-optimally combine the segmentation measurements to better localize and identify boundary pixels of the iris. In this approach, no assumption of circularity is assumed when identifying the iris boundary. 322 measurements were tested and eight were found to contain discriminatory information that can assist in identifying the iris boundary. For occluded images, the iris masks created by the neural network were consistently more accurate than the truth mask created using the circular iris boundary assumption.
Starting Page	46
Ending Page	51
File Size	3440274
Page Count	6
File Format	PDF
ISBN	9781424427734
DOI	10.1109/CIB.2009.4925685
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-03-30
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image segmentation Eyelashes Artificial neural networks Eyelids Robustness Waveguide discontinuities Reflection Iris recognition Active contours Testing
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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