  Fingerprint presentation attack detection is becoming an increasingly
challenging problem due to the continuous advancement of attack preparation
techniques, which generate realistic-looking fake fingerprint presentations. In
this work, rather than relying on legacy fingerprint images, which are widely
used in the community, we study the usefulness of multiple recently introduced
sensing modalities. Our study covers front-illumination imaging using
short-wave-infrared, near-infrared, and laser illumination; and
back-illumination imaging using near-infrared light. Toward studying the
effectiveness of each of these unconventional sensing modalities and their
fusion for liveness detection, we conducted a comprehensive analysis using a
fully convolutional deep neural network framework. Our evaluation compares
different combination of the new sensing modalities to legacy data from one of
our collections as well as the public LivDet2015 dataset, showing the
superiority of the new sensing modalities in most cases. It also covers the
cases of known and unknown attacks and the cases of intra-dataset and
inter-dataset evaluations. Our results indicate that the power of our approach
stems from the nature of the captured data rather than the employed
classification framework, which justifies the extra cost for hardware-based (or
hybrid) solutions. We plan to publicly release one of our dataset collections.
