  In this work, we propose a novel Cyclic Image Translation Generative
Adversarial Network (CIT-GAN) for multi-domain style transfer. To facilitate
this, we introduce a Styling Network that has the capability to learn style
characteristics of each domain represented in the training dataset. The Styling
Network helps the generator to drive the translation of images from a source
domain to a reference domain and generate synthetic images with style
characteristics of the reference domain. The learned style characteristics for
each domain depend on both the style loss and domain classification loss. This
induces variability in style characteristics within each domain. The proposed
CIT-GAN is used in the context of iris presentation attack detection (PAD) to
generate synthetic presentation attack (PA) samples for classes that are
under-represented in the training set. Evaluation using current
state-of-the-art iris PAD methods demonstrates the efficacy of using such
synthetically generated PA samples for training PAD methods. Further, the
quality of the synthetically generated samples is evaluated using Frechet
Inception Distance (FID) score. Results show that the quality of synthetic
images generated by the proposed method is superior to that of other competing
methods, including StarGan.
