Transformers and Generative Adversarial Networks for Liveness Detection in Multitarget Fingerprint Sensors
Computer Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
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Sensors 2021, 21(3), 699; https://doi.org/10.3390/s21030699
Received: 28 November 2020 / Revised: 30 December 2020 / Accepted: 18 January 2021 / Published: 20 January 2021
(This article belongs to the Special Issue Biometric Sensors and Applications)
Fingerprint-based biometric systems have grown rapidly as they are used for various applications including mobile payments, international border security, and financial transactions. The widespread nature of these systems renders them vulnerable to presentation attacks. Hence, improving the generalization ability of fingerprint presentation attack detection (PAD) in cross-sensor and cross-material setting is of primary importance. In this work, we propose a solution based on a transformers and generative adversarial networks (GANs). Our aim is to reduce the distribution shift between fingerprint representations coming from multiple target sensors. In the experiments, we validate the proposed methodology on the public LivDet2015 dataset provided by the liveness detection competition. The experimental results show that the proposed architecture yields an increase in average classification accuracy from 68.52% up to 83.12% after adaptation.
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MDPI and ACS Style
Sandouka, S.B.; Bazi, Y.; Alajlan, N. Transformers and Generative Adversarial Networks for Liveness Detection in Multitarget Fingerprint Sensors. Sensors 2021, 21, 699. https://doi.org/10.3390/s21030699
AMA Style
Sandouka SB, Bazi Y, Alajlan N. Transformers and Generative Adversarial Networks for Liveness Detection in Multitarget Fingerprint Sensors. Sensors. 2021; 21(3):699. https://doi.org/10.3390/s21030699
Chicago/Turabian StyleSandouka, Soha B.; Bazi, Yakoub; Alajlan, Naif. 2021. "Transformers and Generative Adversarial Networks for Liveness Detection in Multitarget Fingerprint Sensors" Sensors 21, no. 3: 699. https://doi.org/10.3390/s21030699
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