hh.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
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
Very Low-Resolution Iris Recognition Via Eigen-Patch Super-Resolution and Matcher Fusion
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
University of Malta, Msida, Malta.
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
2016 (English)In: 2016 IEEE 8th International Conference on Biometrics Theory, Applications and Systems (BTAS), 2016Conference paper (Refereed)
Abstract [en]

Current research in iris recognition is moving towards enabling more relaxed acquisition conditions. This has effects on the quality of acquired images, with low resolution being a predominant issue. Here, we evaluate a super-resolution algorithm used to reconstruct iris images based on Eigen-transformation of local image patches. Each patch is reconstructed separately, allowing better quality of enhanced images by preserving local information. Contrast enhancement is used to improve the reconstruction quality, while matcher fusion has been adopted to improve iris recognition performance. We validate the system using a database of 1,872 near-infrared iris images. The presented approach is superior to bilinear or bicubic interpolation, especially at lower resolutions, and the fusion of the two systems pushes the EER to below 5% for down-sampling factors up to a image size of only 13×13.

Place, publisher, year, edition, pages
2016.
Keyword [en]
Iris recognition, Image resolution, Image reconstruction, Databases, Training, Image recognition, Face
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:hh:diva-31747DOI: 10.1109/BTAS.2016.7791208ISBN: 978-1-4673-9733-9 (print)ISBN: 978-1-4673-9734-6 (print)OAI: oai:DiVA.org:hh-31747DiVA: diva2:952051
Conference
8th IEEE International Conference on Biometrics: Theory, Applications, and Systems, Niagara Falls, Buffalo, USA, September 6-9, 2016
Funder
Swedish Research CouncilKnowledge Foundation
Note

Funding: EU COST Action IC1106. Author F. A.-F. also thanks the Swedish Research Council for funding his research, and the CAISR program of the Swedish Knowledge Foundation.

Available from: 2016-08-11 Created: 2016-08-11 Last updated: 2017-01-10Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full text

Search in DiVA

By author/editor
Alonso-Fernandez, FernandoBigun, Josef
By organisation
CAISR - Center for Applied Intelligent Systems Research
Signal Processing

Search outside of DiVA

GoogleGoogle Scholar

Altmetric score

Total: 77 hits
CiteExportLink to record
Permanent link

Direct link
Cite
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