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

TL;DR

This paper introduces deep GAN architectures, including cGAN and cpGAN, to improve cross-spectral and cross-resolution iris recognition by translating and embedding iris images across different spectra and resolutions.

Contribution

It proposes novel GAN-based methods that jointly address cross-spectral and cross-resolution iris matching, a significant advancement over existing techniques.

Findings

Enhanced accuracy in cross-spectral iris recognition

Effective handling of resolution differences in iris images

Introduction of cGAN and cpGAN architectures for iris matching

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.