  The primary purpose of a fingerprint recognition system is to ensure a
reliable and accurate user authentication, but the security of the recognition
system itself can be jeopardized by spoof attacks. This study addresses the
problem of developing accurate, generalizable, and efficient algorithms for
detecting fingerprint spoof attacks. Specifically, we propose a deep
convolutional neural network based approach utilizing local patches centered
and aligned using fingerprint minutiae. Experimental results on three
public-domain LivDet datasets (2011, 2013, and 2015) show that the proposed
approach provides state-of-the-art accuracies in fingerprint spoof detection
for intra-sensor, cross-material, cross-sensor, as well as cross-dataset
testing scenarios. For example, in LivDet 2015, the proposed approach achieves
99.03% average accuracy over all sensors compared to 95.51% achieved by the
LivDet 2015 competition winners. Additionally, two new fingerprint presentation
attack datasets containing more than 20,000 images, using two different
fingerprint readers, and over 12 different spoof fabrication materials are
collected. We also present a graphical user interface, called Fingerprint Spoof
Buster, that allows the operator to visually examine the local regions of the
fingerprint highlighted as live or spoof, instead of relying on only a single
score as output by the traditional approaches.
