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When Facial Recognition Systems become Presentation Attack Detectors

dc.contributor.authorLazaro Janier Gonzalez-Soler, Kevin Abadi Barhaugen
dc.contributor.editorBrömme, Arslan
dc.contributor.editorDamer, Naser
dc.contributor.editorGomez-Barrero, Marta
dc.contributor.editorRaja, Kiran
dc.contributor.editorRathgeb, Christian
dc.contributor.editorSequeira Ana F.
dc.contributor.editorTodisco, Massimiliano
dc.contributor.editorUhl, Andreas
dc.date.accessioned2022-10-27T10:19:32Z
dc.date.available2022-10-27T10:19:32Z
dc.date.issued2022
dc.description.abstractRecently, biometric systems (BSs) have experienced a broad development mainly due to the great success of deep learning approaches. Generally, most BS provide high security and efficiency. However, they are still vulnerable to attack presentations (APs). To overcome such security issues, these schemes include a Presentation Attack Detection (PAD) module which determines whether the input sample stems from an AP or a bona fide presentation (BP). Traditionally, most PAD subsystems assess the biometric sample prior to the recognition module. In this work, we evaluate to what extent the inverted combination, where the biometric recognition module filters samples prior to the assessment of a PAD mechanism, leads to an overall PAD performance improvement. The experimental evaluation conducted over two well-known databases including challenging attacks, reports a significant improvement in the detection performance when input samples were first filtered by the biometric recognition: only 1% of the APs are accepted while at most 5% BPs are rejected by the PAD subsystem.en
dc.identifier.doi10.1109/BIOSIG55365.2022.9897049
dc.identifier.isbn978-3-88579-723-4
dc.identifier.pissn1617-5500
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/39711
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofBIOSIG 2022
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-329
dc.subjectBiometric systems
dc.subjectPresentation Attack Detection
dc.subjectFace
dc.subjectInteroperability
dc.titleWhen Facial Recognition Systems become Presentation Attack Detectorsen
dc.typeText/Conference Paper
gi.citation.endPage316
gi.citation.publisherPlaceBonn
gi.citation.startPage309
gi.conference.date14.-16. September 2022
gi.conference.locationDarmstadt
gi.conference.sessiontitleFurther Conference Contributions

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