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Cross-spectral iris recognition is emerging as a promising biometric approachto authenticating the identity of individuals. However, matching iris imagesacquired at different spectral bands shows significant performance degradationwhen compared to single-band near-infrared (NIR) matching due to the spectralgap between iris images obtained in the NIR and visual-light (VIS) spectra.Although researchers have recently focused on deep-learning-based approaches torecover invariant representative features for more accurate recognitionperformance, the existing methods cannot achieve the expected accuracy requiredfor commercial applications. Hence, in this paper, we propose a conditionalcoupled generative adversarial network (CpGAN) architecture for cross-spectraliris recognition by projecting the VIS and NIR iris images into alow-dimensional embedding domain to explore the hidden relationship betweenthem. The conditional CpGAN framework consists of a pair of GAN-based networks,one responsible for retrieving images in the visible domain and otherresponsible for retrieving images in the NIR domain. Both networks try to mapthe data into a common embedding subspace to ensure maximum pair-wisesimilarity between the feature vectors from the two iris modalities of the samesubject. To prove the usefulness of our proposed approach, extensiveexperimental results obtained on the PolyU dataset are compared to existingstate-of-the-art cross-spectral recognition methods.

Cross-Spectral Iris Matching Using Conditional Coupled GAN