hh.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • 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
Iris Super-Resolution Using Iterative Neighbor Embedding
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.ORCID iD: 0000-0002-4929-1262
2017 (English)In: 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops / [ed] Lisa O’Conner, Los Alamitos: IEEE Computer Society, 2017, p. 655-663Conference paper, Published paper (Refereed)
Abstract [en]

Iris recognition research is heading towards enabling more relaxed acquisition conditions. This has effects on the quality and resolution of acquired images, severely affecting the accuracy of recognition systems if not tackled appropriately. In this paper, we evaluate a super-resolution algorithm used to reconstruct iris images based on iterative neighbor embedding of local image patches which tries to represent input low-resolution patches while preserving the geometry of the original high-resolution space. To this end, the geometry of the low- and high-resolution manifolds are jointly considered during the reconstruction process. We validate the system with a database of 1,872 near-infrared iris images, while fusion of two iris comparators has been adopted to improve recognition performance. The presented approach is substantially superior to bilinear/bicubic interpolations at very low resolutions, and it also outperforms a previous PCA-based iris reconstruction approach which only considers the geometry of the low-resolution manifold during the reconstruction process. © 2017 IEEE

Place, publisher, year, edition, pages
Los Alamitos: IEEE Computer Society, 2017. p. 655-663
Keywords [en]
Iris recognition, Image reconstruction, Image resolution, Manifolds, Training, Databases, Iris
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:hh:diva-33864DOI: 10.1109/CVPRW.2017.94ISI: 000426448300088Scopus ID: 2-s2.0-85030244663ISBN: 978-1-5386-0733-6 (electronic)ISBN: 978-1-5386-0734-3 (print)OAI: oai:DiVA.org:hh-33864DiVA, id: diva2:1096742
Conference
International Conference on Computer Vision and Pattern Recognition, CVPR, IEEE Computer Society Workshop on Biometrics, Hawaii Convention Center HI, USA, 21-26 Jul, 2017
Projects
SIDUS-AIR
Funder
Swedish Research Council, 2012-4313Knowledge Foundation, SIDUSKnowledge Foundation, CAISRAvailable from: 2017-05-18 Created: 2017-05-18 Last updated: 2020-02-03Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records

Alonso-Fernandez, FernandoBigun, Josef

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

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 1324 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • 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