Logo des Repositoriums
 

Shuffled Patch-Wise Supervision for Presentation Attack Detection

Kurzanzeige
dc.contributor.authorKantarcı, Alperen
dc.contributor.authorDertli, Hasan
dc.contributor.authorEkenel, Hazım Kemal
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.accessioned2021-10-04T08:43:52Z
dc.date.available2021-10-04T08:43:52Z
dc.date.issued2021
dc.description.abstractFace anti-spoofing is essential to prevent false facial verification by using a photo, video, mask, or a different substitute for an authorized person's face. Most of the state-of-the-art presentation attack detection (PAD) systems suffer from overfitting, where they achieve near-perfect scores on a single dataset but fail on a different dataset with more realistic data. This problem drives researchers to develop models that perform well under real-world conditions. This is an especially challenging problem for frame-based presentation attack detection systems that use convolutional neural networks (CNN). To this end, we propose a new PAD approach, which combines pixel-wise binary supervision with patch-based CNN. We believe that training a CNN with face patches allows the model to distinguish spoofs without learning background or dataset-specific traces. We tested the proposed method both on the standard benchmark datasets ---Replay-Mobile, OULU-NPU--- and on a real-world dataset. The proposed approach shows its superiority on challenging experimental setups. Namely, it achieves higher performance on OULU-NPU protocol 3, 4 and on inter-dataset real-world experiments.en
dc.identifier.isbn978-3-88579-709-8
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/37473
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-315
dc.subjectFace antispoofing
dc.subjectPresentation attack detection
dc.subjectConvolutional neural networks
dc.subjectReal-world dataset
dc.titleShuffled Patch-Wise Supervision for Presentation Attack Detectionen
dc.typeText/Conference Paper
gi.citation.endPage70
gi.citation.publisherPlaceBonn
gi.citation.startPage61
gi.conference.date15.-17. September 2021
gi.conference.locationInternational Digital Conference
gi.conference.sessiontitleRegular Research Papers

Dateien

Originalbündel
1 - 1 von 1
Vorschaubild
Name:
biosig2021_proceedings_07.pdf
Größe:
307.57 KB
Format:
Adobe Portable Document Format
Herunterladen

Sammlungen

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