Welcome to the InfoSci Platform
Reference Hub10
An Efficient Iris Recognition System Based on Intersecting Cortical Model Neural Network

An Efficient Iris Recognition System Based on Intersecting Cortical Model Neural Network

Guangzhu Xu, Zaifeng Zang
Copyright: © 2008 |Volume: 2 |Issue: 3 |Pages: 14
ISSN: 1557-3958|EISSN: 1557-3966|ISSN: 1557-3958|EISBN13: 9781615201938|EISSN: 1557-3966|DOI: 10.4018/jcini.2008070104
Cite Article Cite Article

MLA

Xu, Guangzhu, and Zaifeng Zang. "An Efficient Iris Recognition System Based on Intersecting Cortical Model Neural Network." IJCINI vol.2, no.3 2008: pp.43-56. http://doi.org/10.4018/jcini.2008070104

APA

Xu, G. & Zang, Z. (2008). An Efficient Iris Recognition System Based on Intersecting Cortical Model Neural Network. International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 2(3), 43-56. http://doi.org/10.4018/jcini.2008070104

Chicago

Xu, Guangzhu, and Zaifeng Zang. "An Efficient Iris Recognition System Based on Intersecting Cortical Model Neural Network," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI) 2, no.3: 43-56. http://doi.org/10.4018/jcini.2008070104

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

Iris recognition has been shown to be very accurate for human identification. In this article, an efficient iris recognition system based on Intersecting Cortical Model (ICM) neural network is presented which includes two parts mainly. The first part is image preprocessing which has three steps. First, iris location is implemented based on local areas. Then the localized iris area is normalized into rectangular region with a fixed size. At last the iris image enhancement is implemented. In the second part, the ICM neural network is used to generate iris codes and the Hamming Distance between two iris codes is calculated to measure the dissimilarity of them. In order to evaluate the performance of the proposed algorithm, CASIA v1.0 iris image database is used and the recognition results are encouraging.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.