Deep GAN-Based Cross-Spectral Cross-Resolution Iris Recognition

• URL: http://arxiv.org/abs/2108.01569v1
• Date: Tue, 3 Aug 2021 15:30:04 GMT
• Title: Deep GAN-Based Cross-Spectral Cross-Resolution Iris Recognition
• Authors: Moktari Mostofa, Salman Mohamadi, Jeremy Dawson, and Nasser M. Nasrabadi
• Abstract summary: Cross-spectral iris recognition has emerged as a promising biometric approach to establish the identity of individuals. matching iris images acquired at different spectral bands (i.e., matching a visible (VIS) iris probe to a gallery of near-infrared (NIR) iris images or vice versa) shows a significant performance degradation. We have investigated a range of deep convolutional generative adversarial network (DCGAN) architectures to further improve the accuracy of cross-spectral iris recognition methods.
• Score: 15.425678759101203
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, cross-spectral iris recognition has emerged as a promising biometric approach to establish the identity of individuals. However, matching iris images acquired at different spectral bands (i.e., matching a visible (VIS) iris probe to a gallery of near-infrared (NIR) iris images or vice versa) shows a significant performance degradation when compared to intraband NIR matching. Hence, in this paper, we have investigated a range of deep convolutional generative adversarial network (DCGAN) architectures to further improve the accuracy of cross-spectral iris recognition methods. Moreover, unlike the existing works in the literature, we introduce a resolution difference into the classical cross-spectral matching problem domain. We have developed two different techniques using the conditional generative adversarial network (cGAN) as a backbone architecture for cross-spectral iris matching. In the first approach, we simultaneously address the cross-resolution and cross-spectral matching problem by training a cGAN that jointly translates cross-resolution as well as cross-spectral tasks to the same resolution and within the same spectrum. In the second approach, we design a coupled generative adversarial network (cpGAN) architecture consisting of a pair of cGAN modules that project the VIS and NIR iris images into a low-dimensional embedding domain to ensure maximum pairwise similarity between the feature vectors from the two iris modalities of the same subject.

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