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Proceedings Paper

Using artificial neural networks to statistically fuse current iris segmentation techniques to improve limbic boundary localization
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Paper Abstract

One of the basic challenges to robust iris recognition is iris segmentation. This paper proposes the use of an artificial neural network and a feature saliency algorithm to better localize boundary pixels of the iris. No circular boundary assumption is made. A neural network is used to near-optimally combine current iris segmentation methods to more accurate localize the iris boundary. A feature saliency technique is performed to determine which features contain the greatest discriminatory information. Both visual inspection and automated testing showed greater than 98 percent accuracy in determining which pixels in an image of the eye were iris pixels when compared to human determined boundaries.

Paper Details

Date Published: 4 May 2009
PDF: 9 pages
Proc. SPIE 7351, Mobile Multimedia/Image Processing, Security, and Applications 2009, 73510Q (4 May 2009); doi: 10.1117/12.820247
Show Author Affiliations
Randy P. Broussard, U.S. Naval Academy (United States)
Robert W. Ives, U.S. Naval Academy (United States)


Published in SPIE Proceedings Vol. 7351:
Mobile Multimedia/Image Processing, Security, and Applications 2009
Sos S. Agaian; Sabah A. Jassim, Editor(s)

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