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Deep Coupled GAN-Based Score-Level Fusion for Multi-Finger Contact to Contactless Fingerprint Matching

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dc.contributor.authorMd Mahedi Hasan, Nasser Nasrabadi and Jeremy Dawson
dc.contributor.editorBrömme, Arslan
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.accessioned2022-10-27T10:19:27Z
dc.date.available2022-10-27T10:19:27Z
dc.date.issued2022
dc.description.abstractInteroperability between contact to contactless images in fingerprint matching is a key factor in the success of contactless fingerprinting devices, which have recently witnessed an increasing demand for biometric authentication. However, due to the presence of perspective distortion and the absence of elastic deformation in contactless fingerphotos, direct matching between contactless fingerprint probe images and legacy contact-based gallery images produces a low accuracy. In this paper, to improve interoperability, we propose a coupled deep learning framework that consists of two Conditional Generative Adversarial Networks. Generative modeling is employed to find a projection that maximizes the pairwise correlation between these two domains in a common latent embedding subspace. Extensive experiments on three challenging datasets demonstrate significant performance improvements over the state-of-the-art methods and two top-performing commercial off-the-shelf SDKs, i.e., Verifinger 12.0 and Innovatrics. We also achieve a high-performance gain by combining multiple fingers of the same subject using a score fusion model.en
dc.identifier.doi10.1109/BIOSIG55365.2022.9897056
dc.identifier.isbn978-3-88579-723-4
dc.identifier.pissn1617-5482
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/39691
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofBIOSIG 2022
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-329
dc.subjectContactless Fingerphoto Recognition
dc.subjectFingerprint Interoperability
dc.subjectCoupled GAN
dc.titleDeep Coupled GAN-Based Score-Level Fusion for Multi-Finger Contact to Contactless Fingerprint Matchingen
dc.typeText/Conference Paper
gi.citation.endPage161
gi.citation.publisherPlaceBonn
gi.citation.startPage150
gi.conference.date14.-16. September 2022
gi.conference.locationDarmstadt
gi.conference.sessiontitleRegular Research Papers

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P329 - BIOSIG 2022 - Proceedings of the 21st International Conference of the Biometrics Special Interest Group