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dc.contributor.authorPoster, Domenick
dc.contributor.authorNasrabadi, Nasser
dc.contributor.authorRiggan, Benjamin
dc.contributor.editorBrömme, Arslan
dc.contributor.editorBusch, Christoph
dc.contributor.editorDantcheva, Antitza
dc.contributor.editorRathgeb, Christian
dc.contributor.editorUhl, Andreas
dc.date.accessioned2019-06-17T10:00:28Z
dc.date.available2019-06-17T10:00:28Z
dc.date.issued2018
dc.identifier.isbn978-3-88579-676-4
dc.identifier.issn1617-5468
dc.identifier.urihttp://dl.gi.de/handle/20.500.12116/23808
dc.description.abstractDistinguishing between images of irises wearing textured lenses versus those wearing transparent lenses or no lenses is a challenging problem due to the subtle and fine-grained visual differences. Our approach builds upon existing hand-crafted image features and neural network architectures by optimally selecting and combining the most useful set of features into a single model. We build multiple, parallel sub-networks corresponding to the various feature descriptors and learn the best subset of features through group sparsity. We avoid overfitting such a wide and deep model through a selective transfer learning technique and a novel group Dropout regularization strategy. This model achieves roughly a four times increase in performance over the state-of-the-art on three benchmark textured lens datasets and equals the near-perfect state-of-the-art accuracy on two others. Furthermore, the generic nature of the architecture allows it to be extended to other image features, forms of spoofing attacks, or problem domains.en
dc.language.isoen
dc.publisherKöllen Druck+Verlag GmbH
dc.relation.ispartofBIOSIG 2018 - Proceedings of the 17th International Conference of the Biometrics Special Interest Group
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-283
dc.subjectfeature selection
dc.subjectfeature fusion
dc.subjectgroup sparsity
dc.subjectiris liveness detection
dc.subjecttextured contact lens detection
dc.titleDeep Sparse Feature Selection and Fusion for Textured Contact Lens Detectionen
dc.typeText/Conference Paper
dc.pubPlaceBonn
mci.conference.locationDarmstadt
mci.conference.date26.-28. September 2018


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