  We study the problem of fingerprint presentation attack detection (PAD) under
unknown PA materials not seen during PAD training. A dataset of 5,743 bonafide
and 4,912 PA images of 12 different materials is used to evaluate a
state-of-the-art PAD, namely Fingerprint Spoof Buster. We utilize 3D t-SNE
visualization and clustering of material characteristics to identify a
representative set of PA materials that cover most of PA feature space. We
observe that a set of six PA materials, namely Silicone, 2D Paper, Play Doh,
Gelatin, Latex Body Paint and Monster Liquid Latex provide a good
representative set that should be included in training to achieve
generalization of PAD. We also propose an optimized Android app of Fingerprint
Spoof Buster that can run on a commodity smartphone (Xiaomi Redmi Note 4)
without a significant drop in PAD performance (from TDR = 95.7% to 95.3% @ FDR
= 0.2%) which can make a PA prediction in less than 300ms.
