Touchless fingerprint recognition systems are being increasingly used for a fast, hygienic, and distortion-free recognition. However, due to the greater complexity of the algorithms required for processing touchless fingerprint samples, currently only Level 1 and Level 2 features are being used for recognition, and Level 3 features are used only in touch-based optical devices with about 1000 ppi resolution. In this paper, we propose the first innovative method in the literature able to extract Level 3 features, in particular sweat pores, from fingerprint images captured with a touchless acquisition using a commercial off-the-shelf camera. The method uses image processing algorithms to extract a set of candidate sweat pores. Then, computational intelligence techniques based on neural networks are used to learn the local features of the real pores, and select only the actual sweat pores from the set of candidate points. The results show the validity of the proposed methodology, with the majority of the pores correctly extracted, indicating that a touchless fingerprint recognition using Level 3 features is feasible.
Towards touchless pore fingerprint biometrics: a neural approach / A. Genovese, E. Muñoz Ballester, V. Piuri, F. Scotti, G. Sforza - In: Evolutionary Computation (CEC), 2016 IEEE Congress on[s.l] : IEEE, 2016 Nov. - ISBN 9781509006236. - pp. 4265-4272 (( convegno CEC tenutosi a Vancouver nel 2016.
|Titolo:||Towards touchless pore fingerprint biometrics: a neural approach|
GENOVESE, ANGELO (Primo)
|Parole Chiave:||high-resolution; recognition; system; features; authentication; contactless; images|
|Settore Scientifico Disciplinare:||Settore INF/01 - Informatica|
Settore ING-INF/05 - Sistemi di Elaborazione delle Informazioni
|Progetto:||Enforceable Security in the Cloud to Uphold Data Ownership|
|Data di pubblicazione:||nov-2016|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.1109/CEC.2016.7744332|
|Tipologia:||Book Part (author)|
|Appare nelle tipologie:||03 - Contributo in volume|