RT Journal Article
T1 Dynamic fingerprint statistics: Application in presentation attack detection
A1 Husseis, Anas Hussein Ahmad
A1 Liu Jiménez, Judith
A1 Goicoechea Telleria, Inés
A1 Sanchez-Reillo, Raul
AB Fingerprint recognition systems have proven significant performance in many services such as forensics, border control, and mobile applications. Even though fingerprint systems have shown high accuracy and user acceptance, concerns have raised questions about the possibility of having our fingerprint pattern stolen and presented to the system by an imposter. In this paper, we propose a dynamic presentation attack detection mechanism that seeks to mitigate presentation attacks. The adopted mechanism extracts the variation of global fingerprint features in video acquisition scenario and uses it to distinguish bona fide from attack presentations. For that purpose, a dynamic dataset has been collected from 11 independent subjects, 6 fingerprints per user, using thermal and optical sensors. A total of 792 bona fide presentations and 2772 attack presentations are collected. The final PAD subsystem is evaluated based on the standard ISO/. Considering SVM classification and 3 folds cross validation, the obtained error rates at 5% APCER are 18.1% BPCER for the thermal subset and 19.5% BPCER for the optical subset.
PB IEEE
SN 2169-3536
YR 2020
FD 2020-05-20
LK https://hdl.handle.net/10016/33803
UL https://hdl.handle.net/10016/33803
LA eng
NO This work was supported by the European Union's Horizon 2020 for Research and Innovation Program under Grant 675087 (AMBER).
DS e-Archivo
RD 1 sept. 2024