Fingerprint Class Recognition for Securing EMV Transaction

Vibert, Benoît; Bars, Jean-Marie Le; Charrier, Christophe; Rosenberger, Christophe

doi:10.5220/0006205704030410

Cited by 2 publications

(1 citation statement)

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“…Both attacks could be easily detected if the fingerprint class of the probe does not correspond to the legitimate one. Consequently, it is necessary to recognize the fingerprint class from an ISO fingerprint template [ 20 ]. Because the fingerprint image is not always available (not possible to store the fingerprint image in the SE), we consider in this work that we only have the minutiae template to achieve this goal.…”

Section: Fingerprint Type Recognitionmentioning

confidence: 99%

Logical Attacks and Countermeasures for Fingerprint On-Card-Comparison Systems

Vibert

Bars

Charrier

et al. 2020

Sensors

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Digital fingerprints are being used more and more to secure applications for logical and physical access control. In order to guarantee security and privacy trends, a biometric system is often implemented on a secure element to store the biometric reference template and for the matching with a probe template (on-card-comparison). In order to assess the performance and robustness against attacks of these systems, it is necessary to better understand which information could help an attacker successfully impersonate a legitimate user. The first part of the paper details a new attack based on the use of a priori information (such as the fingerprint classification, sensor type, image resolution or number of minutiae in the biometric reference) that could be exploited by an attacker. In the second part, a new countermeasure against brute force and zero effort attacks based on fingerprint classification given a minutiae template is proposed. These two contributions show how fingerprint classification could have an impact for attacks and countermeasures in embedded biometric systems. Experiments show interesting results on significant fingerprint datasets.

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Section: Fingerprint Type Recognitionmentioning

confidence: 99%