Mobile Face Recognition Systems: Exploring Presentation Attack Vulnerability and Usability We have evaluated face recognition software to be used with hand held devices (smartphones). While we can not go into specifics of the systems under test (due to NDAs), we can present the results of our evaluation of liveness detection (or presentation attack detection), matching performance, and success with different complexity levels of attacks. We will contrast the robustness against presentation attacks with the systems usability during regular use, and highlight where currently state of commercial of the shelf systems (COTS) stand in that regard. We will look at the results specifically under the tradeoff between acceptance, linked with usability, and security, which usually negatively impacts usability.

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.