Improving Face AntiSpoofing by 3D Virtual Synthesis Face antispoofing is crucial for the security of face recognition systems. Learning based methods especially deep learning based methods need largescale training samples to reduce overfitting. However, acquiring spoof data is very expensive since the live faces should be reprinted and recaptured in many views. In this paper, we present a method to synthesize virtual spoof data in 3D space to alleviate this problem. Specifically, we consider a printed photo as a flat surface and mesh it into a 3D object, which is then randomly bent and rotated in 3D space. Afterward, the transformed 3D photo is rendered through perspective projection as a virtual sample. The synthetic virtual samples can significantly boost the antispoofing performance when combined with a proposed data balancing strategy. Our promising results open up new possibilities for advancing face antispoofing using cheap and largescale synthetic data.

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.