Large scale experiments on fingerprint liveness detection

Authors:
Gian Luca Marcialis;Luca Ghiani;Katja Vetter;Dirk Morgeneier;Fabio Roli
Affiliations:
Department of Electrical and Electronic Engineering, University of Cagliari, Italy;Department of Electrical and Electronic Engineering, University of Cagliari, Italy;Crossmatch Technologies Inch.;Crossmatch Technologies Inch.;Department of Electrical and Electronic Engineering, University of Cagliari, Italy
Venue:
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Year:
2012

Citing 6
Cited 0

Handbook of Fingerprint Recognition

Handbook of Fingerprint Recognition
Handbook of Biometrics

Handbook of Biometrics
Local binary pattern and wavelet-based spoof fingerprint detection

International Journal of Biometrics
Vitality Detection from Fingerprint Images: A Critical Survey

ICB '07 Proceedings of the international conference on Advances in Biometrics
Fingerprint Liveness Detection Using Curvelet Energy and Co-Occurrence Signatures

CGIV '08 Proceedings of the 2008 Fifth International Conference on Computer Graphics, Imaging and Visualisation
Integrating a wavelet based perspiration liveness check with fingerprint recognition

Pattern Recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

Fingerprint liveness detection consists in extracting measurements, from a fingerprint image, allowing to distinguish between an "alive" fingerprint image, that is, an image coming from the fingertip of the claimed identity, and an artificial replica. Several algorithms have been proposed so far, but the robustness of their performance has not yet been compared when varying several environmental conditions. In this paper, we present a set of experiments investigating the performance of several feature sets designed for fingerprint liveness detection. In particular we assessed the decrease of performance when varying the pressure and the environmental illumination as well as the size of the region of interest (ROI) used for extracting such features. Experimental results on a large data set show the different dependence of some features sets on the investigated conditions.