Fingerprint Matching

Maltoni, Davide; Maio, Dario; Jain, Anil K.; Feng, Jianjiang

doi:10.1007/978-3-030-83624-5_4

Cited by 1 publication

(2 citation statements)

References 244 publications

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“…Nevertheless, a notable observation from the data is the high FNMR, much more significant than typical verification systems without PAD [14]. This distinctive characteristic applies across all participating algorithms and confirms what has been reported in [16], namely, the integration of a PAD algorithm has a substantial impact on the performance of the recognition system.…”

Section: Resultssupporting

confidence: 83%

“…Due to their convenience and security, fingerprint-based authentication systems have garnered significant attention in numerous applications, ranging from financial transactions to healthcare management [14]. However, these systems are vulnerable to various attacks, including spoofing or presentation attacks [13], where attackers use artificial replicas of live fingers to deceive the sensors [10].…”

Section: Introductionmentioning

confidence: 99%

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LivDet2023 - Fingerprint Liveness Detection Competition: Advancing Generalization

Micheletto,

Casula,

Orrù

et al. 2023

2023 IEEE International Joint Conference on Biometrics (IJCB)

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The International Fingerprint Liveness Detection Competition (LivDet) is a biennial event that invites academic and industry participants to prove their advancements in Fingerprint Presentation Attack Detection (PAD). This edition, LivDet2023, proposed two challenges, "Liveness Detection in Action" and "Fingerprint Representation", to evaluate the efficacy of PAD embedded in verification systems and the effectiveness and compactness of feature sets. A third, "hidden" challenge is the inclusion of two subsets in the training set whose sensor information is unknown, testing participants' ability to generalize their models. Only bona fide fingerprint samples were provided to participants, and the competition reports and assesses the performance of their algorithms suffering from this limitation in data availability.* https://sites.unica.it/livdet/

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Section: Resultssupporting

confidence: 83%