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An Open Patch Generator based Fingerprint Presentation Attack Detection using Generative Adversarial Network by Rai, Anuj; Anshul, Ashutosh; Jha, Ashwini; Jain, Prayag; Sharma, Ramprakash; Dey, Somnath is a scholarly article available to read on EtoBox.

What is An Open Patch Generator based Fingerprint Presentation Attack Detection using Generative Adversarial Network about?

The low-cost, user-friendly, and convenient nature of Automatic Fingerprint Recognition Systems (AFRS) makes them suitable for a wide range of applications. This spreading use of AFRS also makes them vulnerable to various security threats. Presentation Attack (PA) or spoofing is one of the threats which is caused by presenting a spoof of a genuine fingerprint to the sensor of AFRS. Fingerprint Presentation Attack Detection (FPAD) is a countermeasure intended to protect AFRS against fake or spoof fingerprints created using various fabrication materials. In this paper, we have proposed a Convolutional Neural Network (CNN) based technique that uses a Generative Adversarial Network (GAN) to augment the dataset with spoof samples generated from the proposed Open Patch Generator (OPG). This OPG is capable of generating realistic fingerprint samples which have no resemblance to the existing spoof fingerprint samples generated with other materials. The augmented dataset is fed to the DenseNet classifier which helps in increasing the performance of the Presentation Attack Detection (PAD) module for the various real-world attacks possible with unknown spoof materials. Experimental evaluation

Author

Rai, Anuj; Anshul, Ashutosh; Jha, Ashwini; Jain, Prayag; Sharma, Ramprakash; Dey, Somnath

Published

2023

Language

EN