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Periocular Recognition Using CNN Features Off-the-Shelf
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.ORCID iD: 0000-0002-1400-346X
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.ORCID iD: 0000-0002-4929-1262
2018 (English)In: 2018 International Conference of the Biometrics Special Interest Group (BIOSIG), Piscataway, N.J.: IEEE, 2018Conference paper, Published paper (Refereed)
Abstract [en]

Periocular 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 offthe-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.

Place, publisher, year, edition, pages
Piscataway, N.J.: IEEE, 2018.
Series
2018 International Conference of the Biometrics Special Interest Group (BIOSIG), ISSN 1617-5468 ; 2018
Keywords [en]
Periocular recognition, deep learning, biometrics, Convolutional Neural Network
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:hh:diva-37704DOI: 10.23919/BIOSIG.2018.8553348ISBN: 978-3-88579-676-3 (print)OAI: oai:DiVA.org:hh-37704DiVA, id: diva2:1238869
Conference
International Conference of the Biometrics Special Interest Group (BIOSIG), Darmstadt, Germany, Sept. 26-29, 2018
Projects
SIDUS-AIR
Funder
Knowledge Foundation, SIDUS-AIRSwedish Research Council, 2016-03497Vinnova, 2018-00472Knowledge Foundation, CAISRAvailable from: 2018-08-14 Created: 2018-08-14 Last updated: 2019-05-23Bibliographically approved

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Hernandez-Diaz, KevinAlonso-Fernandez, FernandoBigun, Josef

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CiteExportLink to record
Permanent link

Direct link
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf