Paper Info
Title
Cross-Spectral Iris Recognition by Learning Device-Specific Band
Abstract
Cross-spectral recognition is still an open challenge in iris recognition. In cross-spectral iris recognition, there exist distinct device-specific bands between near-infrared (NIR) and visible (VIS) images, resulting in the distribution gap between samples from different spectra and thus severe degradation in recognition performance. To tackle this problem, we propose a new cross-spectral iris recognition method to learn spectral-invariant features by estimating device-specific bands. In the proposed method, <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</b> abor <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</b> rident <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</b> etwork (GTN) first utilizes the Gabor function’s priors to perceive iris textures under different spectra, and then codes the device-specific band as the residual component to assist the generation of spectral-invariant features. By investigating the device-specific band, GTN effectively reduces the impact of device-specific bands on identity features. Besides, we make three efforts to further reduce the distribution gap. First, <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</b> pectral <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</b> dversarial <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</b> etwork (SAN) adopts a class-level adversarial strategy to align feature distributions. Second, <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</b> ample- <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</b> nchor (SA) loss upgrades triplet loss by pulling samples to their class center and pushing away from other class centers. Third, we develop a higher-order alignment loss to measures the distribution gap according to space bases and distribution shapes. Extensive experiments on five iris datasets demonstrate the efficacy of our proposed method for cross-spectral iris recognition.
Year
DOI
Venue
2022
10.1109/TCSVT.2021.3117291
IEEE Transactions on Circuits and Systems for Video Technology
Keywords
DocType
Volume
Iris recognition,device-specific band,cross-spectral recognition,adversarial strategy
Journal
32
Issue
ISSN
Citations 
6
1051-8215
0
PageRank 
References 
Authors
0.34
34
5
Name
Order
Citations
PageRank
Jianze Wei121.03
Yunlong Wang232.39
Yi Li312722.03
Ran He41790108.39
Zhenan Sun52379139.49