Show simple item record

dc.contributor.authorAlmeida, Waldir
dc.contributor.authorAndaló, Fernanda
dc.contributor.authorPadilha, Rafael
dc.contributor.authorBertocco, Gabriel
dc.contributor.authorDias, William
dc.contributor.authorTorres, Ricardo Da Silva
dc.contributor.authorWainer, Jacques
dc.contributor.authorRocha, Anderson
dc.identifier.citationPLOS ONE. 2020, 15 (9), .en_US
dc.description.abstractWith the widespread use of biometric authentication comes the exploitation of presentation attacks, possibly undermining the effectiveness of these technologies in real-world setups. One example takes place when an impostor, aiming at unlocking someone else’s smartphone, deceives the built-in face recognition system by presenting a printed image of the user. In this work, we study the problem of automatically detecting presentation attacks against face authentication methods, considering the use-case of fast device unlocking and hardware constraints of mobile devices. To enrich the understanding of how a purely software-based method can be used to tackle the problem, we present a solely data-driven approach trained with multi-resolution patches and a multi-objective loss function crafted specifically to the problem. We provide a careful analysis that considers several user-disjoint and cross-factor protocols, highlighting some of the problems with current datasets and approaches. Such analysis, besides demonstrating the competitive results yielded by the proposed method, provides a better conceptual understanding of the problem. To further enhance efficacy and discriminability, we propose a method that leverages the available gallery of user data in the device and adapts the method decision-making process to the user’s and the device’s own characteristics. Finally, we introduce a new presentation-attack dataset tailored to the mobile-device setup, with real-world variations in lighting, including outdoors and low-light sessions, in contrast to existing public datasets.en_US
dc.publisherPublic Library of Scienceen_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.titleDetecting face presentation attacks in mobile devices with a patch-based CNN and a sensor-aware loss functionen_US
dc.title.alternativeDetecting face presentation attacks in mobile devices with a patch-based CNN and a sensor-aware loss functionen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.source.journalPLOS ONEen_US

Files in this item


This item appears in the following Collection(s)

Show simple item record

Navngivelse 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Navngivelse 4.0 Internasjonal