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Impact of Image Context for Single Deep Learning Face Morphing Attack Detection

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dc.contributor.authorJoana Pimenta, Iurii Medvedev
dc.contributor.editorDamer, Naser
dc.contributor.editorGomez-Barrero, Marta
dc.contributor.editorRaja, Kiran
dc.contributor.editorRathgeb, Christian
dc.contributor.editorSequeira, Ana F.
dc.contributor.editorTodisco, Massimiliano
dc.contributor.editorUhl, Andreas
dc.date.accessioned2023-12-12T10:46:47Z
dc.date.available2023-12-12T10:46:47Z
dc.date.issued2023
dc.description.abstractThe increase in security concerns due to technological advancements has led to the popularity of biometric approaches that utilize physiological or behavioral characteristics for enhanced recognition. Face recognition systems (FRSs) have become prevalent, but they are still vulnerable to image manipulation techniques such as face morphing attacks. This study investigates the impact of the alignment settings of input images on deep learning face morphing detection performance. We analyze the interconnections between the face contour and image context and suggest optimal alignment conditions for face morphing detection.en
dc.identifier.isbn978-3-88579-733-3
dc.identifier.issn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/43272
dc.language.isoen
dc.pubPlaceBonn
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofBIOSIG 2023
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-339
dc.subjectMorphing
dc.subjectBiometric performance measurement; Datasets
dc.subjectEvaluation
dc.subjectBenchmarking
dc.titleImpact of Image Context for Single Deep Learning Face Morphing Attack Detectionen
dc.typeText/Conference Paper
mci.conference.date20.-22. September 2023
mci.conference.locationDarmstadt
mci.conference.sessiontitleFurther Conference Contributions
mci.reference.pages237-246

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P339 - BIOSIG 2023 - Proceedings of the 22nd International Conference of the Biometrics Special Interest Group