Comparative study of minutiae selection methods for digital fingerprints.

Biometric systems are more and more used for many applications (physical access control, e-payment, etc.). Digital fingerprint is an interesting biometric modality as it can easily be used for embedded systems (smartcard, smartphone, and smartwatch). A fingerprint template is composed of a set of minutiae used for their comparison. In embedded systems, a secure element is in general used to store and compare fingerprint templates to meet security and privacy requirements. Nevertheless, it is necessary to select a subset of minutiae from a template due to storage and computation constraints. In this study, we present, a comparative study of the main minutiae selection methods from the literature. The considered methods require no further information like the raw image. Experimental results show their relative performance when using different matching algorithms and datasets. We identified that some methods can be used within different contexts (enrollment or verification) with minimal degradation of performance.

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

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.