  While the performance of face recognition systems has improved significantly
in the last decade, they are proved to be highly vulnerable to presentation
attacks (spoofing). Most of the research in the field of face presentation
attack detection (PAD), was focused on boosting the performance of the systems
within a single database. Face PAD datasets are usually captured with RGB
cameras, and have very limited number of both bona-fide samples and
presentation attack instruments. Training face PAD systems on such data leads
to poor performance, even in the closed-set scenario, especially when
sophisticated attacks are involved.
  We explore two paths to boost the performance of the face PAD system against
challenging attacks. First, by using multi-channel (RGB, Depth and NIR) data,
which is still easily accessible in a number of mass production devices.
Second, we develop a novel Autoencoders + MLP based face PAD algorithm.
Moreover, instead of collecting more data for training of the proposed deep
architecture, the domain adaptation technique is proposed, transferring the
knowledge of facial appearance from RGB to multi-channel domain. We also
demonstrate, that learning the features of individual facial regions, is more
discriminative than the features learned from an entire face. The proposed
system is tested on a very recent publicly available multi-channel PAD database
with a wide variety of presentation attacks.
