  The increased need for unattended authentication in multiple scenarios has
motivated a wide deployment of biometric systems in the last few years. This
has in turn led to the disclosure of security concerns specifically related to
biometric systems. Among them, Presentation Attacks (PAs, i.e., attempts to log
into the system with a fake biometric characteristic or presentation attack
instrument) pose a severe threat to the security of the system: any person
could eventually fabricate or order a gummy finger or face mask to impersonate
someone else. The biometrics community has thus made a considerable effort to
the development of automatic Presentation Attack Detection (PAD) mechanisms,
for instance through the international LivDet competitions.
  In this context, we present a novel fingerprint PAD scheme based on $i)$ a
new capture device able to acquire images within the short wave infrared (SWIR)
spectrum, and $ii)$ an in-depth analysis of several state-of-the-art techniques
based on both handcrafted and deep learning features. The approach is evaluated
on a database comprising over 4700 samples, stemming from 562 different
subjects and 35 different presentation attack instrument (PAI) species. The
results show the soundness of the proposed approach with a detection equal
error rate (D-EER) as low as 1.36\% even in a realistic scenario where five
different PAI species are considered only for testing purposes (i.e., unknown
attacks).
