Fingerprint Liveness Detection using Convolutional Neural Networks

ABSTRACT:

With the developing utilization of biometric validation frameworks in the ongoing years, parody unique mark recognition has turned out to be progressively vital. In this paper, we utilize convolutional neural systems (CNNs) for unique finger impression liveness recognition. Our framework is assessed on the informational collections utilized in the liveness identification rivalry of the years 2009, 2011, and 2013, which involves very nearly 50 000 genuine and counterfeit fingerprints pictures. We look at four changed models: two CNNs pretrained on normal pictures and calibrated with the unique mark pictures, CNN with arbitrary weights, and a traditional neighborhood paired example approach.

We demonstrate that pretrained CNNs can yield the best in class results with no requirement for design or hyperparameter determination. Informational collection expansion is utilized to expand the classifiers execution, for profound models as well as for shallow ones. We likewise report great precision on little preparing sets (400 examples) utilizing these expansive pretrained systems. Our best model accomplishes a general rate of 97.1% of accurately ordered examples a relative change of 16% in test mistake when contrasted and the best beforehand distributed outcomes. This model won the primary prize in the unique mark liveness location rivalry 2015 with a general precision of 95.5%.

BASE PAPER: Evaluating software-based fingerprint

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