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Proceedings Paper

Image quality assessment for iris biometric
Author(s): Nathan D. Kalka; Jinyu Zuo; Natalia A. Schmid; Bojan Cukic
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Paper Abstract

Iris recognition, the ability to recognize and distinguish individuals by their iris pattern, is the most reliable biometric in terms of recognition and identification performance. However, performance of these systems is affected by poor quality imaging. In this work, we extend previous research efforts on iris quality assessment by analyzing the effect of seven quality factors: defocus blur, motion blur, off-angle, occlusion, specular reflection, lighting, and pixel-counts on the performance of traditional iris recognition system. We have concluded that defocus blur, motion blur, and off-angle are the factors that affect recognition performance the most. We further designed a fully automated iris image quality evaluation block that operates in two steps. First each factor is estimated individually, then the second step involves fusing the estimated factors by using Dempster-Shafer theory approach to evidential reasoning. The designed block is tested on two datasets, CASIA 1.0 and a dataset collected at WVU. Considerable improvement in recognition performance is demonstrated when removing poor quality images evaluated by our quality metric. The upper bound on processing complexity required to evaluate quality of a single image is O(n2 log n), that of a 2D-Fast Fourier Transform.

Paper Details

Date Published: 17 April 2006
PDF: 11 pages
Proc. SPIE 6202, Biometric Technology for Human Identification III, 62020D (17 April 2006); doi: 10.1117/12.666448
Show Author Affiliations
Nathan D. Kalka, West Virginia Univ. (United States)
Jinyu Zuo, West Virginia Univ. (United States)
Natalia A. Schmid, West Virginia Univ. (United States)
Bojan Cukic, 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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