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.
Cite
Text
Yadav et al. "Synthesizing Iris Images Using RaSGAN with Application in Presentation Attack Detection." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2019. doi:10.1109/CVPRW.2019.00297Markdown
[Yadav et al. "Synthesizing Iris Images Using RaSGAN with Application in Presentation Attack Detection." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2019.](https://mlanthology.org/cvprw/2019/yadav2019cvprw-synthesizing/) doi:10.1109/CVPRW.2019.00297BibTeX
@inproceedings{yadav2019cvprw-synthesizing,
title = {{Synthesizing Iris Images Using RaSGAN with Application in Presentation Attack Detection}},
author = {Yadav, Shivangi and Chen, Cunjian and Ross, Arun},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2019},
pages = {2422-2430},
doi = {10.1109/CVPRW.2019.00297},
url = {https://mlanthology.org/cvprw/2019/yadav2019cvprw-synthesizing/}
}