Combining Multiple oneclass Classifiers for Anomaly based Face Spoofing Attack Detection Oneclass spoofing detection approaches have been an effective alternative to the twoclass learners in the face presentation attack detection particularly in unseen attack scenarios. We propose an ensemble based anomaly detection approach applicable to oneclass classifiers. A new score normalisation method is proposed to normalise the output of individual outlier detectors before fusion. To comply with the accuracy and diversity objectives for the component classifiers, three different strategies are utilised to build a pool of anomaly experts. To boost the performance, we also make use of the clientspecific information both in the design of individual experts as well as in setting a distinct threshold for each client. We carry out extensive experiments on three face antispoofing datasets and show that the proposed ensemble approaches are comparable superior to the techniques based on the twoclass formulation or classindependent settings. *

Generalized Presentation Attack Detection: a face antispoofing evaluation proposal 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 antispoofing research, generalization in real scenarios remains a challenge. In this paper we present a new opensource evaluation framework to study the generalization capacity of face PAD methods, coined here as faceGPAD. 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.