A Novel Fingerprint Biometric Cryptosystem Based on Convolutional Neural Networks

Srđan Barzut, Milan Milosavljević, Saša Adamović, Muzafer Saračević, Nemanja Maček and Milan Gnjatović
Additional contact information
Srđan Barzut: Tehnikum Taurunum Department, Academy of Applied Technical Studies Belgrade, Nade Dimić 4, 11080 Belgrade, Serbia
Milan Milosavljević: Faculty of Informatics and Computing, Singidunum University, Danijelova 32, 11000 Belgrade, Serbia
Saša Adamović: Faculty of Informatics and Computing, Singidunum University, Danijelova 32, 11000 Belgrade, Serbia
Muzafer Saračević: Department of Computer Sciences, University of Novi Pazar, Dimitrija Tucovića bb, 36300 Novi Pazar, Serbia
Nemanja Maček: School of Electrical and Computer Engineering, Academy of Technical and Art Applied Studies, Vojvode Stepe 283, 11000 Belgrade, Serbia
Milan Gnjatović: Department of Information Technology, University of Criminal Investigation and Police Studies, Cara Dušana 196, 11080 Belgrade, Serbia

Mathematics, 2021, vol. 9, issue 7, 1-12

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.

Keywords: biometric cryptosystem; fuzzy commitment scheme; fingerprint recognition; machine learning; convolutional neural network (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/9/7/730/pdf (application/pdf)
https://www.mdpi.com/2227-7390/9/7/730/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:9:y:2021:i:7:p:730-:d:525623

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().