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A study on iris textural correlation using steering kernels

Hu, Yang and Sirlantzis, Konstantinos and Howells, Gareth (2016) A study on iris textural correlation using steering kernels. In: 2016 IEEE 8th International Conference on Biometrics Theory, Applications and Systems (BTAS). IEEE. ISBN 978-1-4673-9734-6. E-ISBN 978-1-4673-9733-9. (doi:10.1109/BTAS.2016.7791160)

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https://dx.doi.org/10.1109/BTAS.2016.7791160

Abstract

Research on iris recognition have observed that iris texture has inherent radial correlation. However, currently, there lacks a deeper insight into iris textural correlation. Few research focus on a quantitative and comprehensive analysis on this correlation. In this paper, we perform a quantitative analysis on iris textural correlation. We employ steering kernels to model the textural correlation in images. We conduct experiments on three benchmark datasets covering iris captures with varying quality. We find that the local textural correlation varies due to local characteristics in iris images, while the general trend of textural correlation goes along the radial direction. Moreover, we demonstrate that the information on iris textural correlation can be utilized to improve iris recognition. We employ this information to produce iris codes. We show that the iris code with the information on textural correlation achieves an improved performance compared to traditional iris codes.

Item Type: Book section
DOI/Identification number: 10.1109/BTAS.2016.7791160
Uncontrolled keywords: Biometrics, iris recognition
Subjects: T Technology > TK Electrical engineering. Electronics. Nuclear engineering > TK7800 Electronics > TK7880 Applications of electronics > TK7882.B56 Biometric identification
T Technology > TK Electrical engineering. Electronics. Nuclear engineering > TK7800 Electronics > TK7880 Applications of electronics > TK7882.P3 Pattern recognition systems
Divisions: Faculties > Sciences > School of Engineering and Digital Arts > Image and Information Engineering
Depositing User: Gareth Howells
Date Deposited: 31 Oct 2016 20:25 UTC
Last Modified: 25 Sep 2019 10:49 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/58293 (The current URI for this page, for reference purposes)
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