Fingerprint Liveness Detection From Single Image Using Low-Level Features and Shape Analysis


Fingerprint-based mostly authentication systems have developed rapidly within the recent years. But, current fingerprint-based mostly biometric systems are prone to spoofing attacks. Moreover, single feature-based static approach will not perform equally over totally different fingerprint sensors and spoofing materials. In this paper, we have a tendency to propose a static software approach. We propose to mix low-level gradient options from speeded-up strong features, pyramid extension of the histograms of oriented gradient and texture features from Gabor wavelet using dynamic score level integration. We extract these options from a single fingerprint image to overcome the problems faced in dynamic software approaches, that require user cooperation and longer computational time. A experimental analysis done on LivDet 2011 information created a mean equal error rate (EER) of 3.ninety fivepercent over four databases. The result outperforms the present best average EER of 9.625%. We also performed experiments with LivDet 2013 database and achieved a median classification error rate of 2.27percent as compared with 12.87p.c obtained by the LivDet 2013 competition winner.

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