Experimental results on fingerprint liveness detection
AMDO'12 Proceedings of the 7th international conference on Articulated Motion and Deformable Objects
Proceedings of the on Multimedia and security
Neurocomputing
Large scale experiments on fingerprint liveness detection
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Multi-scale local binary pattern with filters for spoof fingerprint detection
Information Sciences: an International Journal
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This paper proposes a new curvelet transform-based method to detect spoof fingerprint attacks in fingerprint biometric systems. It uses only one image to differentiate a real fingerprint from a spoof one. It is based on the observation that, real and spoof fingerprints exhibit different textural characteristics. Textural measures based on curvelet energy signatures and curvelet co-occurrence signatures are used to characterize fingerprint texture. Dimensionalities of the feature sets are reduced by running Pudil's Sequential Forward Floating Selection (SFFS) algorithm. We test two feature sets independently on various classifiers like: AdaBoost. M1, support vector machine and k-nearest neighbor; then we fuse all the mentioned classifiers using the "Majority Voting Rule" to form an Ensemble classifier. Classification rates achieved with these classifiers for energy signatures range from ~94.12% to ~97.41%. Likewise, classification rates for co-occurrence signatures range from ~94.35% to ~98.12%. Thus, the performance of a new liveness detection approach is very promising, as it needs only one fingerprint and no extra hardware to detect vitality.