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dc.contributor.authorArashloo, Shervin Rahimzadehen_US
dc.date.accessioned2021-03-09T06:30:11Z
dc.date.available2021-03-09T06:30:11Z
dc.date.issued2020
dc.identifier.issn1051-8215
dc.identifier.urihttp://hdl.handle.net/11693/75896
dc.description.abstractWe address the face presentation attack detection problem in the challenging conditions of an unseen attack scenario where the system is exposed to novel presentation attacks that were not available in the training stage. To this aim, we pose the unseen face presentation attack detection (PAD) problem as the one-class kernel Fisher null-space regression and present a new face PAD approach that only uses bona fide (genuine) samples for training. Drawing on the proposed kernel Fisher null-space face PAD method and motivated by the availability of multiple information sources, next, we propose a multiple kernel fusion anomaly detection approach to combine the complementary information provided by different views of the problem for improved detection performance. And the last but not the least, we introduce a sparse variant of our multiple kernel Fisher null-space face PAD approach to improve inference speed at the operational phase without compromising much on the detection performance. The results of an experimental evaluation on the OULU-NPU, Replay-Mobile, Replay-Attack and MSU-MFSD datasets illustrate that the proposed method outperforms other methods operating in an unseen attack detection scenario while achieving very competitive performance to multi-class methods (that benefit from presentation attack data for training) despite using only bona fide samples in the training stage.en_US
dc.language.isoEnglishen_US
dc.source.titleIEEE Transactions on Circuits and Systems for Video Technologyen_US
dc.relation.isversionofhttps://dx.doi.org/10.1109/TCSVT.2020.3046505en_US
dc.subjectFace presentation attack detectionen_US
dc.subjectAnti-spoofingen_US
dc.subjectUnseen attacksen_US
dc.subjectNovelty detectionen_US
dc.subjectOne-class classificationen_US
dc.subjectKernel regressionen_US
dc.subjectMultiple kernel fusionen_US
dc.subjectSparse regularisationen_US
dc.titleUnseen face presentation attack detection using sparse multiple kernel fisher null-spaceen_US
dc.typeArticleen_US
dc.departmentDepartment of Computer Engineeringen_US
dc.identifier.doi10.1109/TCSVT.2020.3046505en_US
dc.publisherIEEEen_US
dc.contributor.bilkentauthorArashloo, Shervin Rahimzadeh


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