  We present a style-transfer based wrapper, called Universal Material
Generator (UMG), to improve the generalization performance of any fingerprint
spoof detector against spoofs made from materials not seen during training.
Specifically, we transfer the style (texture) characteristics between
fingerprint images of known materials with the goal of synthesizing fingerprint
images corresponding to unknown materials, that may occupy the space between
the known materials in the deep feature space. Synthetic live fingerprint
images are also added to the training dataset to force the CNN to learn
generative-noise invariant features which discriminate between lives and
spoofs. The proposed approach is shown to improve the generalization
performance of a state-of-the-art spoof detector, namely Fingerprint Spoof
Buster, from TDR of 75.24% to 91.78% @ FDR = 0.2%. These results are based on a
large-scale dataset of 5,743 live and 4,912 spoof images fabricated using 12
different materials. Additionally, the UMG wrapper is shown to improve the
average cross-sensor spoof detection performance from 67.60% to 80.63% when
tested on the LivDet 2017 dataset. Training the UMG wrapper requires only 100
live fingerprint images from the target sensor, alleviating the time and
resources required to generate large-scale live and spoof datasets for a new
sensor. We also fabricate physical spoof artifacts using a mixture of known
spoof materials to explore the role of cross-material style transfer in
improving generalization performance.
