  The one-class anomaly detection approach has previously been found to be
effective in face presentation attack detection, especially in an
\textit{unseen} attack scenario, where the system is exposed to novel types of
attacks. This work follows the same anomaly-based formulation of the problem
and analyses the merits of deploying \textit{client-specific} information for
face spoofing detection. We propose training one-class client-specific
classifiers (both generative and discriminative) using representations obtained
from pre-trained deep convolutional neural networks. Next, based on
subject-specific score distributions, a distinct threshold is set for each
client, which is then used for decision making regarding a test query. Through
extensive experiments using different one-class systems, it is shown that the
use of client-specific information in a one-class anomaly detection formulation
(both in model construction as well as decision threshold tuning) improves the
performance significantly. In addition, it is demonstrated that the same set of
deep convolutional features used for the recognition purposes is effective for
face presentation attack detection in the class-specific one-class anomaly
detection paradigm.
