Generation of Privacy-friendly Datasets of Latent Fingerprint Images using Generative Adversarial Networks

Stefan Seidlitz; Kris Jürgens; Andrey Makrushin; Christian Kraetzer; Jana Dittmann

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Paper

Generation of Privacy-friendly Datasets of Latent Fingerprint Images using Generative Adversarial Networks

Topics: Image Formation, Acquisition Devices and Sensors; Multimedia Forensics

In Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4 VISAPP: VISAPP, 345-352, 2021

Authors: Stefan Seidlitz ; Kris Jürgens ; Andrey Makrushin ; Christian Kraetzer and Jana Dittmann

Affiliation: Otto-von-Guericke University Magdeburg, Universitaetsplatz 2, Magdeburg, Germany

Keyword(s): Digitized Forensics, Latent Fingerprint, Image Synthesis, Generative Adversarial Networks, GAN, Privacy.

Abstract: The restrictions posed by the recent trans-border regulations to the usage of biometric data force researchers in the fields of digitized forensics and biometrics to use synthetic data for development and evaluation of new algorithms. For digitized forensics, we introduce a technique for conversion of privacy-sensitive datasets of real latent fingerprints to "privacy-friendly" datasets of synthesized fingerprints. Privacy-friendly means in our context that the generated fingerprint images cannot be linked to a particular person who provided fingerprints to the original dataset. In contrast to the standard fingerprint generation approach that makes use of mathematical modeling for drawing ridge-line patterns, we propose applying a data-driven approach making use of generative adversarial neural networks (GAN). In our synthesis experiments the performance of three established GAN architectures is examined. The NIST Special Database 27 is exemplary used as a data source of real latent f ingerprints. The set of training images is augmented by applying filters from the StirTrace benchmarking tool. The suitability of the generated fingerprint images is checked with the NIST fingerprint image quality tool (NFIQ2). The unlinkability to any original fingerprint is established by evaluating outcomes of the NIST fingerprint matching tool. (More)

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Paper citation in several formats:

Seidlitz, S., Jürgens, K., Makrushin, A., Kraetzer, C. and Dittmann, J. (2021). Generation of Privacy-friendly Datasets of Latent Fingerprint Images using Generative Adversarial Networks. In Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 4: VISAPP; ISBN 978-989-758-488-6; ISSN 2184-4321, SciTePress, pages 345-352. DOI: 10.5220/0010251603450352

@conference{visapp21,
author={Stefan Seidlitz and Kris Jürgens and Andrey Makrushin and Christian Kraetzer and Jana Dittmann},
title={Generation of Privacy-friendly Datasets of Latent Fingerprint Images using Generative Adversarial Networks},
booktitle={Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 4: VISAPP},
year={2021},
pages={345-352},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010251603450352},
isbn={978-989-758-488-6},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 4: VISAPP
TI - Generation of Privacy-friendly Datasets of Latent Fingerprint Images using Generative Adversarial Networks
SN - 978-989-758-488-6
IS - 2184-4321
AU - Seidlitz, S.
AU - Jürgens, K.
AU - Makrushin, A.
AU - Kraetzer, C.
AU - Dittmann, J.
PY - 2021
SP - 345
EP - 352
DO - 10.5220/0010251603450352
PB - SciTePress