Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Handbook of Fingerprint Recognition
Handbook of Fingerprint Recognition
Liveness Detection for Fingerprint Scanners Based on the Statistics of Wavelet Signal Processing
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Handbook 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
Texture and Wavelet-Based Spoof Fingerprint Detection for Fingerprint Biometric Systems
ICETET '08 Proceedings of the 2008 First International Conference on Emerging Trends in Engineering and Technology
Analysis of Fingerprint Pores for Vitality Detection
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
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The aim of fingerprint liveness detection is to detect if a fingerprint image, sensed by an electronic device, belongs to an alive fingertip or to an artificial replica of it. It is well-known that a fingerprint can be replicated and standard electronic sensors cannot distinguish between a replica and an alive fingerprint image. Accordingly, several countermeasures in terms of fingerprint liveness detection algorithms have been proposed, but their performance is not yet acceptable. However, no works studied the possibility of combining different feature sets, thus exploiting the eventual complementarity among them. In this paper, we show some preliminary experiments on feature-level fusion of several algorithms, including a novel feature set proposed by the authors. Experiments are carried out on the datasets available at Second International Fingerprint Liveness Detection Competition (LivDet 2011). Reported results clearly show that multiple feature sets allow improving the liveness detection performance.