  In the last decades, the broad development experienced by biometric systems
has unveiled several threats which may decrease their trustworthiness. Those
are attack presentations which can be easily carried out by a non-authorised
subject to gain access to the biometric system. In order to mitigate those
security concerns, most face Presentation Attack Detection techniques have
reported a good detection performance when they are evaluated on known
Presentation Attack Instruments (PAI) and acquisition conditions, in contrast
to more challenging scenarios where unknown attacks are included in the test
set. For those more realistic scenarios, the existing algorithms face
difficulties to detect unknown PAI species in many cases. In this work, we use
a new feature space based on Fisher Vectors, computed from compact Binarised
Statistical Image Features histograms, which allow discovering semantic feature
subsets from known samples in order to enhance the detection of unknown
attacks. This new representation, evaluated for challenging unknown attacks
taken from freely available facial databases, shows promising results: a
BPCER100 under 17% together with an AUC over 98% can be achieved in the
presence of unknown attacks. In addition, by training a limited number of
parameters, our method is able to achieve state-of-the-art deep learning-based
approaches for cross-dataset scenarios.
