Fingerprint generation and presentation attack detection using deep neural networks

초록

Performance evaluation of fingerprint recognition systems requires large-scale databases. Unfortunately, collecting fingerprints is expensive and time-consuming, and publishing them is restricted due to the privacy protection legislation. Hence, an algorithm which can generate huge fingerprint datasets would be of great help. With the popularization of fingerprint authentication systems, detecting fake fingerprints, also known as presentation attack detection, is an essential problem. Inspired by the fast development of deep learning, this paper demonstrates novel algorithms to generate artificial fingerprints and detect fake fingerprints using deep neural networks. The experimental results prove that the proposed system can generate fingerprints which have the same characteristics as real fingerprints. Regarding presentation attack detection, the proposed system shows an average detection error rate of 1.57% on three LivDet databases, including LivDet 2011, 2013, and 2015.

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