High Fidelity Fingerprint Generation: Quality, Uniqueness, and Privacy

TL;DR

This paper introduces the Clarkson Fingerprint Generator (CFG), a GAN-based model that produces high-quality, diverse, and privacy-preserving synthetic fingerprints at high resolution, useful for biometric research and applications.

Contribution

The paper presents a novel GAN architecture for generating realistic, high-resolution fingerprints that are unique and do not compromise training data privacy.

Findings

Generated fingerprints are high fidelity and resemble real data.

The synthetic fingerprints are diverse and maintain privacy.

The model and dataset are publicly available for research.

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 https://github.com/keivanB/Clarkson_Finger_Gen