Image quality assessment for iris biometric

Nathan D. Kalka; Jinyu Zuo; Natalia A. Schmid; Bojan Cukic

doi:10.1117/12.666448

17 April 2006 Image quality assessment for iris biometric

Nathan D. Kalka, Jinyu Zuo, Natalia A. Schmid, Bojan Cukic

Proceedings Volume 6202, Biometric Technology for Human Identification III; 62020D (2006) https://doi.org/10.1117/12.666448
Event: Defense and Security Symposium, 2006, Orlando (Kissimmee), Florida, United States

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.

Citation Download Citation

Nathan D. Kalka, Jinyu Zuo, Natalia A. Schmid, and Bojan Cukic "Image quality assessment for iris biometric", Proc. SPIE 6202, Biometric Technology for Human Identification III, 62020D (17 April 2006); https://doi.org/10.1117/12.666448

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available