Synthesizing Iris Images Using RaSGAN With Application in Presentation Attack Detection

Shivangi Yadav, Cunjian Chen, Arun Ross; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2019, pp. 0-0

In this work we design a new technique for generating synthetic iris images and demonstrate its potential for presentation attack detection (PAD). The proposed technique utilizes the generative capability of a Relativistic Average Standard Generative Adversarial Network (RaSGAN) to synthesize high quality images of the iris. Unlike traditional GANs, RaSGAN enhances the generative power of the network by introducing a "relativistic" discriminator (and generator), which aims to maximize the probability that the real input data is more realistic than the synthetic data (and vice-versa, respectively). The resultant generated images are observed to be very similar to real iris images. Furthermore, we demonstrate the viability of using these synthetic images to train a PAD system that can generalize well to "unseen" attacks, i.e., the PAD system is able to detect attacks that were not used during the training phase.