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High Fidelity Fingerprint Generation: Quality, Uniqueness, and Privacy

2021-05-21 15:18:28
Keivan Bahmani, Richard Plesh, Peter Johnson, Stephanie Schuckers, Timothy Swyka
arXiv_CV
     
Abstract

In this work, we utilize progressive growth-based Generative Adversarial Networks (GANs) to develop the Clarkson Fingerprint Generator (CFG). We demonstrate that the CFG is capable of generating realistic, high fidelity, $512\times512$ pixels, full, plain impression fingerprints. Our results suggest that the fingerprints generated by the CFG are unique, diverse, and resemble the training dataset in terms of minutiae configuration and quality, while not revealing the underlying identities of the training data. We make the pre-trained CFG model and the synthetically generated dataset publicly available at this https URL

Abstract (translated)

URL

https://arxiv.org/abs/2105.10403

PDF

https://arxiv.org/pdf/2105.10403.pdf

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