Synthesizing Iris Images Using RaSGAN With Application in Presentation Attack Detection

iWarpGAN: Disentangling Identity and Style to Generate Synthetic Iris Images
Shivangi Yadav, Cunjian Chen and Arun Ross
https://openaccess.thecvf.com/content_CVPRW_2019/papers/Biometrics/Yadav_Synthesizing_Iris_Images_Using_RaSGAN_With_Application_in_Presentation_Attack_CVPRW_2019_paper.pdf

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.

Release notes

This repository is a Pytorch implementation of RaSGAN that utilizes features from https://github.com/AlexiaJM/RelativisticGAN/tree/master

Requirements

• Linux and Windows are supported, but we recommend Linux for performance and compatibility reasons.
• 64-bit Python 3.8 and PyTorch 1.9.0 (or later). See https://pytorch.org for PyTorch install instructions.
• CUDA toolkit. (Why is a separate CUDA toolkit installation required? See Troubleshooting).

Getting started

Training

You can train new networks using RaSGAN.py. For example:

# RaSGAN model to generate images using noise as input
python RaSGAN.py --path=['./Extra/1', './Train_Data'] --input_folder=Train_Data --output_folder=Output_Folder

References:

1. The relativistic discriminator: a key element missing from standard GAN, Jolicoeur-Martineau et al. 2017

Citation

@InProceedings{Yadav_2019_CVPR_Workshops,
author = {Yadav, Shivangi and Chen, Cunjian and Ross, Arun},
title = {Synthesizing Iris Images Using RaSGAN With Application in Presentation Attack Detection},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2019}
}

Development

This is code is under development to improve the network and its performance