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Proceedings Paper

Active shape models for effective iris segmentation
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Paper Abstract

Iris recognition has been demonstrated to be an efficient technology for doing personal identification. Performance of iris recognition system depends on the isolation of the iris region from rest of the eye image. In this work, effective use of active shape models (ASMs) for doing iris segmentation is demonstrated. A method for building flexible model by learning patterns of iris invariability from a well organized training set is described. The specific approach taken in the work sacrifices generality, in order to accommodate better iris segmentation. The algorithm was initially applied on the on-angle, noise free CASIA data base and then was extended to the off-axis iris images collected at WVU eye center. A direct comparison with canny iris segmentation in terms of error rates, demonstrate effectiveness of ASM segmentation. For the selected threshold value of 0.4, FAR and FRR values were 0.13% and 0.09% using canny detectors and 0% each using the proposed ASM based method.

Paper Details

Date Published: 17 April 2006
PDF: 10 pages
Proc. SPIE 6202, Biometric Technology for Human Identification III, 62020H (17 April 2006); doi: 10.1117/12.666435
Show Author Affiliations
Aditya Abhyankar, Clarkson Univ. (United States)
Stephanie Schuckers, Clarkson Univ. (United States)
West Virginia Univ. (United States)


Published in SPIE Proceedings Vol. 6202:
Biometric Technology for Human Identification III
Patrick J. Flynn; Sharath Pankanti, Editor(s)

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