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Fisher Vector Encoding of Dense-BSIF Features for Unknown Face Presentation Attack Detection

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dc.contributor.authorGonzález-Soler, Lázaro J.
dc.contributor.authorGomez-Barrero, Marta
dc.contributor.authorBusch, Christoph
dc.contributor.editorBrömme, Arslan
dc.contributor.editorBusch, Christoph
dc.contributor.editorDantcheva, Antitza
dc.contributor.editorRaja, Kiran
dc.contributor.editorRathgeb, Christian
dc.contributor.editorUhl, Andreas
dc.date.accessioned2020-09-16T08:25:49Z
dc.date.available2020-09-16T08:25:49Z
dc.date.issued2020
dc.description.abstractThe task of determining whether a sample stems from a real subject (i.e, it is a bona fide presentation) or it comes from an artificial replica (i.e., it is an attack presentation) is a mandatory requirement for biometric capture devices, which has received a lot of attention in the recent past. Nowadays, most face Presentation Attack Detection (PAD) approaches have reported a good detection performance when they are evaluated on known Presentation Attack Instruments (PAIs) and acquisition conditions, in contrast to more challenging scenarios where unknown attacks are included in the evaluation. For those more realistic scenarios, the existing approaches are in many cases unable to detect unknown PAI species. In this work, we introduce a new feature space based on Fisher vectors, computed from compact Binarised Statistical Image Features (BSIF) histograms, which allows finding semantic feature subsets from known samples in order to enhance the detection of unknown attacks. This new representation, evaluated over three freely available facial databases, shows promising results in the top state-of-the-art: a BPCER100 under 17% together with a AUC over 98% can be achieved in the presence of unknown attacks.en
dc.identifier.isbn978-3-88579-700-5
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/34343
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofBIOSIG 2020 - Proceedings of the 19th International Conference of the Biometrics Special Interest Group
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-306
dc.subjectPresentation attack detection
dc.subjectprobabilistic visual vocabulary
dc.subjectcommon feature space
dc.subjectunknown attacks
dc.subjectface.
dc.titleFisher Vector Encoding of Dense-BSIF Features for Unknown Face Presentation Attack Detectionen
dc.typeText/Conference Paper
gi.citation.endPage43
gi.citation.publisherPlaceBonn
gi.citation.startPage33
gi.conference.date16.-18. September 2020
gi.conference.locationInternational Digital Conference
gi.conference.sessiontitleRegular Research Papers

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P306 - BIOSIG 2020 - Proceedings of the 19th International Conference of the Biometrics Special Interest Group