  Prevailing fingerprint recognition systems are vulnerable to spoof attacks.
To mitigate these attacks, automated spoof detectors are trained to distinguish
a set of live or bona fide fingerprints from a set of known spoof fingerprints.
Despite their success, spoof detectors remain vulnerable when exposed to
attacks from spoofs made with materials not seen during training of the
detector. To alleviate this shortcoming, we approach spoof detection as a
one-class classification problem. The goal is to train a spoof detector on only
the live fingerprints such that once the concept of "live" has been learned,
spoofs of any material can be rejected. We accomplish this through training
multiple generative adversarial networks (GANS) on live fingerprint images
acquired with the open source, dual-camera, 1900 ppi RaspiReader fingerprint
reader. Our experimental results, conducted on 5.5K spoof images (from 12
materials) and 11.8K live images show that the proposed approach improves the
cross-material spoof detection performance over state-of-the-art one-class and
binary class spoof detectors on 11 of 12 testing materials and 7 of 12 testing
materials, respectively.
