Logo des Repositoriums
 

QualFace: Adapting Deep Learning Face Recognition for ID and Travel Documents with Quality Assessment

Kurzanzeige
dc.contributor.authorTremoço, João
dc.contributor.authorMedvedev, Iurii
dc.contributor.authorGonçalves, Nuno
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.accessioned2021-10-04T08:43:44Z
dc.date.available2021-10-04T08:43:44Z
dc.date.issued2021
dc.description.abstractModern face recognition biometrics widely rely on deep neural networks that are usually trained on large collections of wild face images of celebrities. This choice of the data is related with its public availability in a situation when existing ID document compliant face image datasets (usually stored by national institutions) are hardly accessible due to continuously increasing privacy restrictions. However this may lead to a leak in performance in systems developed specifically for ID document compliant images. In this work we proposed a novel face recognition approach for mitigating that problem. To adapt deep face recognition network for document security purposes, we propose to regularise the training process with specific sample mining strategy which penalises the samples by their estimated quality, where the quality metric is proposed by our work and is related to the specific case of face images for ID documents. We perform extensive experiments and demonstrate the efficiency of proposed approach for ID document compliant face images.en
dc.identifier.isbn978-3-88579-709-8
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/37449
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 recognition
dc.subjectbiometric template
dc.subjectdocument security
dc.titleQualFace: Adapting Deep Learning Face Recognition for ID and Travel Documents with Quality Assessmenten
dc.typeText/Conference Paper
gi.citation.endPage158
gi.citation.publisherPlaceBonn
gi.citation.startPage147
gi.conference.date15.-17. September 2021
gi.conference.locationInternational Digital Conference
gi.conference.sessiontitleRegular Research Papers

Dateien

Originalbündel
1 - 1 von 1
Vorschaubild nicht verfügbar
Name:
biosig2021_proceedings_15.pdf
Größe:
754.58 KB
Format:
Adobe Portable Document Format
Herunterladen

Sammlungen

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