Synthesizing Iris Images Using RaSGAN with Application in Presentation Attack Detection
Abstract
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.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jun 16, 2019
- Accession Number
- AD1157643
Entities
People
- Arun Ross
- Cunjian Chen
- Shivangi Yadav
Organizations
- Michigan State University