Identity-Preserving GAN for Cross Spectral Iris Recognition

Cross spectral iris recognition has been shown to cause a degradation in iris matching scenarios due to the inherent differences between the NIR and visible spectra. This led us to explore methods of iris domain translation, allowing us to generate images between the NIR and visible domains using generative adversarial networks (GANs). We train a GAN network with an additional classifier component to act as an identity-preserving module allowing the generator to produce not only high quality, but identity-specific images. We apply this method on three cross-spectral iris datasets, namely, the Cross-eyed-cross-spectral iris database, the PolyU bi-spectral database and the WVU Multispectral database collected from our lab. We implement image enhancement techniques on the cropped iris images and unrolled, normalized iris images, allowing for the generator to learn the iris texture with minimal noise surrounding the iris and to show the performance of the generated images in different matching scenarios. We show the performance of our model by matching the generated iris images against the true iris images in their translated domain. We show that applying this image translation technique as a preprocessing step increases the matching performance when applied to iris matching software, such as Neurotechnology’s commercial iris recognition software, VeriEye and an open-source iris recognition software, OSIRIS. Lastly, we perform an ablation study for each set of experiments by removing the classifier component and comparing the results with our model, showing that the competition between the generator and classifier has an important role in learning identity-specific features.