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dc.contributor.authorAbduh, Latifah
dc.contributor.authorLuma, Omar
dc.contributor.authorIvrissimtzis, Ioannis
dc.contributor.editorSkala, Václav
dc.date.accessioned2023-10-03T16:55:31Z
dc.date.available2023-10-03T16:55:31Z
dc.date.issued2023
dc.identifier.citationJournal of WSCG. 2023, vol. 31, no. 1-2, p. 91-98.en
dc.identifier.issn1213 – 6972 (hard copy)
dc.identifier.issn1213 – 6980 (CD-ROM)
dc.identifier.issn1213 – 6964 (on-line)
dc.identifier.urihttp://hdl.handle.net/11025/54288
dc.format8 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherVáclav Skala - UNION Agencyen
dc.rights© Václav Skala - UNION Agencyen
dc.subjectútok na prezentaci obličejecs
dc.subjecttransformátor viděnícs
dc.subjectResNetcs
dc.subjectdetekce anomáliícs
dc.subjectjednotřídní klasifikacecs
dc.titleAnomaly Detection with Transformers in Face Anti-spoofingen
dc.typearticleen
dc.typečlánekcs
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedTransformers are emerging as the new gold standard in various computer vision applications, and have already been used in face anti-spoofing demonstrating competitive performance. In this paper, we propose a network with the ViT transformer and ResNet as the backbone for anomaly detection in face anti-spoofing, and compare the performance of various one-class classifiers at the end of the pipeline, such as one-class SVM, Isolation Forest, and decoders. Test results on the RA and SiW databases show the proposed approach to be competitive as an anomaly detection method for face anti-spoofing.en
dc.subject.translatedface presentation attacken
dc.subject.translatedvision transformeren
dc.subject.translatedResNeten
dc.subject.translatedanomaly detectionen
dc.subject.translatedone-class classificationen
dc.identifier.doihttps://www.doi.org/10.24132/JWSCG.2023.10
dc.type.statusPeer-revieweden
Appears in Collections:Volume 31, Number 1-2 (2023)

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Please use this identifier to cite or link to this item: http://hdl.handle.net/11025/54288

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