Improving Crossdatabase Face Presentation Attack Detection via Adversarial Domain Adaptation Face recognition (FR) is being widely used in many applications from access control to smartphone unlock. As a result, face presentation attack detection (PAD) has drawn increasing attentions to secure the FR systems. Traditional approaches for PAD mainly assume that training and testing scenarios are similar in imaging conditions (illumination, scene, camera sensor, etc.), and thus may lack good generalization capability into new application scenarios. In this work, we propose an endtoend learning approach to improve PAD generalization capability by utilizing prior knowledge from source domain via adversarial domain adaptation. We first build a source domain PAD model optimized with triplet loss. Subsequently, we perform adversarial domain adaptation w.r.t. the target domain to learn a shared embedding space by both the source and target domain models, in which the discriminator cannot reliably predict whether a sample is from the source or target domain. Finally, PAD in the target domain is performed with knearest neighbors (kNN) classifier in the embedding space. The proposed approach shows promising generalization capability in a number of publicdomain face PAD databases.

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.