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

Randy P. Broussard; Robert W. Ives

doi:10.1117/12.820247

4 May 2009 Using artificial neural networks to statistically fuse current iris segmentation techniques to improve limbic boundary localization

Randy P. Broussard, Robert W. Ives

Author Affiliations +

Proceedings Volume 7351, Mobile Multimedia/Image Processing, Security, and Applications 2009; 73510Q (2009) https://doi.org/10.1117/12.820247
Event: SPIE Defense, Security, and Sensing, 2009, Orlando, Florida, United States

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.

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Randy P. Broussard and Robert W. Ives "Using artificial neural networks to statistically fuse current iris segmentation techniques to improve limbic boundary localization", Proc. SPIE 7351, Mobile Multimedia/Image Processing, Security, and Applications 2009, 73510Q (4 May 2009); https://doi.org/10.1117/12.820247

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KEYWORDS

Iris recognition

Neural networks

Image segmentation

Hough transforms

Artificial neural networks

Iris

Image processing

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