  Nowadays, the adoption of face recognition for biometric authentication
systems is usual, mainly because this is one of the most accessible biometric
modalities. Techniques that rely on trespassing these kind of systems by using
a forged biometric sample, such as a printed paper or a recorded video of a
genuine access, are known as presentation attacks, but may be also referred in
the literature as face spoofing. Presentation attack detection is a crucial
step for preventing this kind of unauthorized accesses into restricted areas
and/or devices. In this paper, we propose a novel approach which relies in a
combination between intrinsic image properties and deep neural networks to
detect presentation attack attempts. Our method explores depth, salience and
illumination maps, associated with a pre-trained Convolutional Neural Network
in order to produce robust and discriminant features. Each one of these
properties are individually classified and, in the end of the process, they are
combined by a meta learning classifier, which achieves outstanding results on
the most popular datasets for PAD. Results show that proposed method is able to
overpass state-of-the-art results in an inter-dataset protocol, which is
defined as the most challenging in the literature.
