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Exploring the Untapped Potential of Unsupervised Representation Learning for Training Set Agnostic Finger Vein Recognition

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dc.contributor.authorTugce Arican, Raymond Veldhuis
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.accessioned2023-12-12T10:46:46Z
dc.date.available2023-12-12T10:46:46Z
dc.date.issued2023
dc.description.abstractFinger vein patterns are a promising biometric trait because of their higher privacy and security features compared to face and finger prints. Finger vein recognition methods have been researched extensively, especially deep learning based methods such as Convolutional Neural Networks. These methods show promising recognition performance, but their low degree of generalization and adaptability results in much lower and inconsistent recognition performance in cross database scenarios. Despite these drawbacks, much less research has gone into the generalization and adaptability of these deep learning methods. This study addresses these issues and proposes an unsupervised learning approach, namely a patch-based Convolutional Auto-encoder for learning finger vein representations. Our proposed approach outperforms traditional baseline finger recognition methods on the UTFVP, SDUMLA-HMT, and PKU datasets, and achieves state-of-the-art performance on the UTFVP dataset with 0.24\% EER. It also indicates a noticeably higher generalization of finger vein features across different datasets compared to a supervised method. The findings of this work offer promising advancements in achieving robust finger vein recognition in real-life scenarios, due to the enhanced generalization and adaptability of our proposed method.en
dc.identifier.isbn978-3-88579-733-3
dc.identifier.issn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/43263
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.subjectBiometric performance measurement
dc.subjectPeriocular
dc.subjectEar
dc.subjectPalm
dc.subjectand Vein
dc.titleExploring the Untapped Potential of Unsupervised Representation Learning for Training Set Agnostic Finger Vein Recognitionen
dc.typeText/Conference Paper
mci.conference.date20.-22. September 2023
mci.conference.locationDarmstadt
mci.conference.sessiontitleRegular Research Papers
mci.reference.pages156-167

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