@article{
author = "Barzut, Srđan and Milosavljević, Milan and Adamović, Saša and Saračević, Muzafer and Maček, Nemanja and Gnjatović, Milan",
year = "2021",
abstract = "Modern access controls employ biometrics as a means of authentication to a great extent.
For example, biometrics is used as an authentication mechanism implemented on commercial devices
such as smartphones and laptops. This paper presents a fingerprint biometric cryptosystem based on
the fuzzy commitment scheme and convolutional neural networks. One of its main contributions
is a novel approach to automatic discretization of fingerprint texture descriptors, entirely based on
a convolutional neural network, and designed to generate fixed-length templates. By converting
templates into the binary domain, we developed the biometric cryptosystem that can be used in
key-release systems or as a template protection mechanism in fingerprint matching biometric systems.
The problem of biometric data variability is marginalized by applying the secure block-level Bose–
Chaudhuri–Hocquenghem error correction codes, resistant to statistical-based attacks. The evaluation
shows significant performance gains when compared to other texture-based fingerprint matching
and biometric cryptosystems.",
publisher = "Basel, Switzerland : MDPI",
journal = "Mathematics",
title = "A Novel Fingerprint Biometric Cryptosystem Based on Convolutional Neural Networks",
volume = "9",
number = "7",
pages = "730",
doi = "10.3390/math9070730"
}