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Periocular Recognition Using CNN Features Off-the-Shelf

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dc.contributor.authorHernandez-Diaz, Kevin
dc.contributor.authorAlonso-Fernandez, Fernando
dc.contributor.authorBigun, Josef
dc.contributor.editorBrömme, Arslan
dc.contributor.editorBusch, Christoph
dc.contributor.editorDantcheva, Antitza
dc.contributor.editorRathgeb, Christian
dc.contributor.editorUhl, Andreas
dc.date.accessioned2019-06-17T10:00:24Z
dc.date.available2019-06-17T10:00:24Z
dc.date.issued2018
dc.description.abstractPeriocular refers to the region around the eye, including sclera, eyelids, lashes, brows and skin. With a surprisingly high discrimination ability, it is the ocular modality requiring the least constrained acquisition. Here, we apply existing pre-trained architectures, proposed in the context of the ImageNet Large Scale Visual Recognition Challenge, to the task of periocular recognition. These have proven to be very successful for many other computer vision tasks apart from the detection and classification tasks for which they were designed. Experiments are done with a database of periocular images captured with a digital camera. We demonstrate that these off-the-shelf CNN features can effectively recognize individuals based on periocular images, despite being trained to classify generic objects. Compared against reference periocular features, they show an EER reduction of up to 40%, with the fusion of CNN and traditional features providing additional improvements.en
dc.identifier.isbn978-3-88579-676-4
dc.identifier.pissn1617-5469
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/23798
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.subjectPeriocular recognition
dc.subjectdeep learning
dc.subjectbiometrics
dc.subjectConvolutional Neural Network.
dc.titlePeriocular Recognition Using CNN Features Off-the-Shelfen
dc.typeText/Conference Paper
gi.citation.publisherPlaceBonn
gi.conference.date26.-28. September 2018
gi.conference.locationDarmstadt

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P282 - BIOSIG 2018 - Proceedings of the 17th International Conference of the Biometrics Special Interest Group