Cross-Spectral Iris Matching Using Conditional Coupled GAN

TL;DR

This paper introduces a novel conditional coupled GAN architecture that projects visible and NIR iris images into a shared embedding space, significantly improving cross-spectral iris recognition accuracy.

Contribution

The paper proposes a new CpGAN framework that effectively maps VIS and NIR iris images into a common domain for better matching, addressing spectral gap challenges.

Findings

Outperforms existing methods on PolyU dataset

Achieves higher recognition accuracy in cross-spectral matching

Demonstrates robustness across spectral variations

Abstract

Cross-spectral iris recognition is emerging as a promising biometric approach to authenticating the identity of individuals. However, matching iris images acquired at different spectral bands shows significant performance degradation when compared to single-band near-infrared (NIR) matching due to the spectral gap between iris images obtained in the NIR and visual-light (VIS) spectra. Although researchers have recently focused on deep-learning-based approaches to recover invariant representative features for more accurate recognition performance, the existing methods cannot achieve the expected accuracy required for commercial applications. Hence, in this paper, we propose a conditional coupled generative adversarial network (CpGAN) architecture for cross-spectral iris recognition by projecting the VIS and NIR iris images into a low-dimensional embedding domain to explore the hidden relationship between them. The conditional CpGAN framework consists of a pair of GAN-based networks, one responsible for retrieving images in the visible domain and other responsible for retrieving images in the NIR domain. Both networks try to map the data into a common embedding subspace to ensure maximum pair-wise similarity between the feature vectors from the two iris modalities of the same subject. To prove the usefulness of our proposed approach, extensive experimental results obtained on the PolyU dataset are compared to existing state-of-the-art cross-spectral recognition methods.