Generative Adversarial Networks (GANs) have shown success in approximating
complex distributions for synthetic image generation. However, current
GAN-based methods for generating biometric images, such as iris, have certain
limitations: (a) the synthetic images often closely resemble images in the
training dataset; (b) the generated images lack diversity in terms of the
number of unique identities represented in them; and (c) it is difficult to
generate multiple images pertaining to the same identity. To overcome these
issues, we propose iWarpGAN that disentangles identity and style in the context
of the iris modality by using two transformation pathways: Identity
Transformation Pathway to generate unique identities from the training set, and
Style Transformation Pathway to extract the style code from a reference image
and output an iris image using this style. By concatenating the transformed
identity code and reference style code, iWarpGAN generates iris images with
both inter- and intra-class variations. The efficacy of the proposed method in
generating such iris DeepFakes is evaluated both qualitatively and
quantitatively using ISO/IEC 29794-6 Standard Quality Metrics and the VeriEye
iris matcher. Further, the utility of the synthetically generated images is
demonstrated by improving the performance of deep learning based iris matchers
that augment synthetic data with real data during the training process