A novel framework for cross-spectral iris matching

Mohammed A M Abdullah,Jonathon A Chambers,Satnam S Dlay,Wai L Woo

doi:10.1186/s41074-016-0009-9

Mohammed A M Abdullah, Jonathon A Chambers + Show 2 more

Open Access

https://doi.org/10.1186/s41074-016-0009-9

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Abstract

Abstract Previous work on iris recognition focused on either visible light (VL), near-infrared (NIR) imaging, or their fusion. However, limited numbers of works have investigated cross-spectral matching or compared the iris biometric performance under both VL and NIR spectrum using unregistered iris images taken from the same subject. To the best of our knowledge, this is the first work that proposes a framework for cross-spectral iris matching using unregistered iris images. To this end, three descriptors are proposed namely, Gabor-difference of Gaussian (G-DoG), Gabor-binarized statistical image feature (G-BSIF), and Gabor-multi-scale Weberface (G-MSW) to achieve robust cross-spectral iris matching. In addition, we explore the differences in iris recognition performance across the VL and NIR spectra. The experiments are carried out on the UTIRIS database which contains iris images acquired with both VL and NIR spectra for the same subject. Experimental and comparison results demonstrate that the proposed framework achieves state-of-the-art cross-spectral matching. In addition, the results indicate that the VL and NIR images provide complementary features for the iris pattern and their fusion improves notably the recognition performance.

Highlights

Among the various traits used for human identification, the iris pattern has gained an increasing amount of attention for its accuracy, reliability, and noninvasive characteristics
The potential of iris biometrics has been affirmed with 1.2 trillion comparison by tests carried out by the National Institute of Standards and Technology (NIST) which confirmed that iris biometrics has the
We propose to integrate the 1D log-Gabor filter [35] with Difference of Gaussian (DoG), Binarized Statistical Image Features (BSIF), and multi-scale Weberface (MSW) to produce the Gabor-difference of Gaussian (G-DoG), Gabor-binarized statistical image feature (G-BSIF), and Gabor-multi-scale Weberface (G-MSW) in addition to decision level fusion to achieve a robust cross-spectral iris recognition

Summary

Introduction

Among the various traits used for human identification, the iris pattern has gained an increasing amount of attention for its accuracy, reliability, and noninvasive characteristics. The performance of iris recognition systems is impressive as demonstrated by Daugman [3] who reported false acceptance rates of only 10−6 on a study of 200 billion cross-comparisons. The potential of iris biometrics has been affirmed with 1.2 trillion comparison by tests carried out by the National Institute of Standards and Technology (NIST) which confirmed that iris biometrics has the. Iris recognition technology nowadays is widely deployed in various large-scale applications such as the border crossing system in the United Arab Emirates, Mexico national ID program, and the Unique Identification Authority of India (UIDAI) project [5]. As a case in point, more than one billion residents have been enrolled in the UIDAI project where about 1015 all-to-all check operations are carried out daily for identity de-duplication using iris biometrics as the main modality [5, 6]

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