Iris recognition using Gabor filters optimized by the particle swarm algorithm

C. C. Tsai; Jin-Shiuh Taur; Chin-Wang Tao

doi:10.1117/1.3134128

1 April 2009 Iris recognition using Gabor filters optimized by the particle swarm algorithm

C. C. Tsai, Jin-Shiuh Taur, Chin-Wang Tao

Author Affiliations +

Journal of Electronic Imaging, Vol. 18, Issue 2, 023009 (April 2009). https://doi.org/10.1117/1.3134128

Abstract

An efficient feature extraction algorithm based on optimized Gabor filters and a relative variation analysis approach is proposed for iris recognition. The Gabor filters are optimized by using the particle swarm algorithm to adjust the parameters. Moreover, a sequential scheme is developed to determine the number of filters in the optimal Gabor filter bank. In the preprocessing step, the lower part of the iris image is unwrapped and normalized to a rectangular block that is then decomposed by the optimal Gabor filters. After that, a simple encoding method is adopted to generate a compact iris code. Experimental results show that with a smaller iris code size, the proposed method can produce comparable performance to that of the existing iris recognition systems.

©(2009) Society of Photo-Optical Instrumentation Engineers (SPIE)

Citation Download Citation

C. C. Tsai, Jin-Shiuh Taur, and Chin-Wang Tao "Iris recognition using Gabor filters optimized by the particle swarm algorithm," Journal of Electronic Imaging 18(2), 023009 (1 April 2009). https://doi.org/10.1117/1.3134128

Published: 1 April 2009

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