  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.
