A Novel Fingerprint Biometric Cryptosystem Based on Convolutional Neural Networks
Само за регистроване кориснике
2021
Аутори
Barzut, SrđanMilosavljević, Milan
Adamović, Saša
Saračević, Muzafer
Maček, Nemanja
Gnjatović, Milan
Чланак у часопису (Објављена верзија)
Метаподаци
Приказ свих података о документуАпстракт
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 pe...rformance gains when compared to other texture-based fingerprint matching
and biometric cryptosystems.
Кључне речи:
biometric cryptosystem / fuzzy commitment scheme / fingerprint recognition / machine learning / convolutional neural networkИзвор:
Mathematics, 2021, 9, 7, 730-Издавач:
- Basel, Switzerland : MDPI
Институција/група
JakovTY - JOUR AU - Barzut, Srđan AU - Milosavljević, Milan AU - Adamović, Saša AU - Saračević, Muzafer AU - Maček, Nemanja AU - Gnjatović, Milan PY - 2021 UR - http://jakov.kpu.edu.rs/handle/123456789/1511 AB - 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. PB - Basel, Switzerland : MDPI T2 - Mathematics T1 - A Novel Fingerprint Biometric Cryptosystem Based on Convolutional Neural Networks VL - 9 IS - 7 SP - 730 DO - 10.3390/math9070730 ER -
@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" }
Barzut, S., Milosavljević, M., Adamović, S., Saračević, M., Maček, N.,& Gnjatović, M.. (2021). A Novel Fingerprint Biometric Cryptosystem Based on Convolutional Neural Networks. in Mathematics Basel, Switzerland : MDPI., 9(7), 730. https://doi.org/10.3390/math9070730
Barzut S, Milosavljević M, Adamović S, Saračević M, Maček N, Gnjatović M. A Novel Fingerprint Biometric Cryptosystem Based on Convolutional Neural Networks. in Mathematics. 2021;9(7):730. doi:10.3390/math9070730 .
Barzut, Srđan, Milosavljević, Milan, Adamović, Saša, Saračević, Muzafer, Maček, Nemanja, Gnjatović, Milan, "A Novel Fingerprint Biometric Cryptosystem Based on Convolutional Neural Networks" in Mathematics, 9, no. 7 (2021):730, https://doi.org/10.3390/math9070730 . .