  Over the past few years, Presentation Attack Detection (PAD) has become a
fundamental part of facial recognition systems. Although much effort has been
devoted to anti-spoofing research, generalization in real scenarios remains a
challenge. In this paper we present a new open-source evaluation framework to
study the generalization capacity of face PAD methods, coined here as
face-GPAD. This framework facilitates the creation of new protocols focused on
the generalization problem establishing fair procedures of evaluation and
comparison between PAD solutions. We also introduce a large aggregated and
categorized dataset to address the problem of incompatibility between publicly
available datasets. Finally, we propose a benchmark adding two novel evaluation
protocols: one for measuring the effect introduced by the variations in face
resolution, and the second for evaluating the influence of adversarial
operating conditions.
