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The low-cost, user-friendly, and convenient nature of Automatic FingerprintRecognition Systems (AFRS) makes them suitable for a wide range ofapplications. This spreading use of AFRS also makes them vulnerable to varioussecurity threats. Presentation Attack (PA) or spoofing is one of the threatswhich is caused by presenting a spoof of a genuine fingerprint to the sensor ofAFRS. Fingerprint Presentation Attack Detection (FPAD) is a countermeasureintended to protect AFRS against fake or spoof fingerprints created usingvarious fabrication materials. In this paper, we have proposed a ConvolutionalNeural Network (CNN) based technique that uses a Generative Adversarial Network(GAN) to augment the dataset with spoof samples generated from the proposedOpen Patch Generator (OPG). This OPG is capable of generating realisticfingerprint samples which have no resemblance to the existing spoof fingerprintsamples generated with other materials. The augmented dataset is fed to theDenseNet classifier which helps in increasing the performance of thePresentation Attack Detection (PAD) module for the various real-world attackspossible with unknown spoof materials. Experimental evaluations of the proposedapproach are carried out on the Liveness Detection (LivDet) 2015, 2017, and2019 competition databases. An overall accuracy of 96.20\%, 94.97\%, and92.90\% has been achieved on the LivDet 2015, 2017, and 2019 databases,respectively under the LivDet protocol scenarios. The performance of theproposed PAD model is also validated in the cross-material and cross-sensorattack paradigm which further exhibits its capability to be used underreal-world attack scenarios.

An Open Patch Generator based Fingerprint Presentation Attack Detection using Generative Adversarial Network