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Learning by Environment Cluster s for Face Presentation Attack Detection

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dc.contributor.authorMatsunami, Tomoaki
dc.contributor.authorUchida, Hidetsugu
dc.contributor.authorAbe, Narishige
dc.contributor.authorYamada, Shigefumi
dc.contributor.editorBrömme, Arslan
dc.contributor.editorBusch, Christoph
dc.contributor.editorDamer, Naser
dc.contributor.editorDantcheva, Antitza
dc.contributor.editorGomez-Barrero, Marta
dc.contributor.editorRaja, Kiran
dc.contributor.editorRathgeb, Christian
dc.contributor.editorSequeira, Ana
dc.contributor.editorUhl, Andreas
dc.date.accessioned2021-10-04T08:43:46Z
dc.date.available2021-10-04T08:43:46Z
dc.date.issued2021
dc.description.abstractFace recognition has been used widely for personal authentication. However, there is a problem that it is vulnerable to a presentation attack in which a counterfeit such as a photo is presented to a camera to impersonate another person. Although various presentation attack detection methods have been proposed, these methods have not been able to sufficiently cope with the diversity of the heterogeneous environments including presentation attack instruments (PAIs) and lighting conditions. In this paper, we propose Learning by Environment Clusters (LEC) which divides training data into some clusters of similar photographic environments and trains bona-fide and attack classification models for each cluster. Experimental results using Replay-Attack, OULU-NPU, and CelebA-Spoof show the EER of the conventional method which trains one classification model from all data was 20.0%, but LEC can achieve 13.8% EER when using binarized statistical image features (BSIFs) and support vector machine used as the classification methoden
dc.identifier.isbn978-3-88579-709-8
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/37456
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofBIOSIG 2021 - Proceedings of the 20th International Conference of the Biometrics Special Interest Group
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-319
dc.subjectface anti-spoofing
dc.subjectpresentation attack detection
dc.subjectface image clustering
dc.titleLearning by Environment Cluster s for Face Presentation Attack Detectionen
dc.typeText/Conference Paper
gi.citation.endPage212
gi.citation.publisherPlaceBonn
gi.citation.startPage205
gi.conference.date15.-17. September 2021
gi.conference.locationInternational Digital Conference
gi.conference.sessiontitleFurther Conference Contributions

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P315 - BIOSIG 2021 - Proceedings of the 20th International Conference of the Biometrics Special Interest Group