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DC poleHodnotaJazyk
dc.contributor.authorDenisova, Anna
dc.contributor.editorSkala, Václav
dc.date.accessioned2022-09-01T07:59:39Z
dc.date.available2022-09-01T07:59:39Z
dc.date.issued2022
dc.identifier.citationWSCG 2022: full papers proceedings: 30. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p.en
dc.identifier.isbn978-80-86943-33-6
dc.identifier.issn2464-4617
dc.identifier.urihttp://hdl.handle.net/11025/49574
dc.format8 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherVáclav Skala - UNION Agencyen
dc.rights© Václav Skala - UNION Agencyen
dc.subjectprezentace detekce útokucs
dc.subjectextrakce funkcícs
dc.subjecthloubková mapucs
dc.subjecttepelná datacs
dc.subjectinfračervená datacs
dc.subjectWMCAcs
dc.subjectSVMcs
dc.subjectRDWT- Haralick-SVMcs
dc.subjectMC-CNNcs
dc.titleAn improved simple feature set for face presentation attack detectionen
dc.typeconferenceObjecten
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedPresentation attacks are weak points of facial biometrical authentication systems. Although several presentation attack detection methods were developed, the best of them require a sufficient amount of training data and rely on computationally intensive deep learning based features. Thus, most of them have difficulties with adaptation to new types of presentation attacks or new cameras. In this paper, we introduce a method for face presentation attack detection with low requirements for training data and high efficiency for a wide range of spoofing attacks. The method includes feature extraction and binary classification stages. We use a combination of simple statistical and texture features and describe the experimental results of feature adjustment and selection. We validate the proposed method using WMCA dataset. The experiments showed that the proposed features decrease the average classification error in comparison with the RDWT-Haralick-SVM method and demonstrate the best performance among non-CNN-based methodsen
dc.subject.translatedpresentation attack detectionen
dc.subject.translatedfeature extractionen
dc.subject.translateddepth mapen
dc.subject.translatedthermal dataen
dc.subject.translatedinfrared dataen
dc.subject.translatedWMCAen
dc.subject.translatedSVMen
dc.subject.translatedRDWT- Haralick-SVMen
dc.subject.translatedMC-CNNen
dc.identifier.doihttps://www.doi.org/10.24132/CSRN.3201.3
dc.type.statusPeer-revieweden
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