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
IrisSeg: A Fast and Robust Iris Segmentation Framework for Non-Ideal Iris Images
Centre for Development of Advanced Computing (CDAC), Mumbai, India.
Centre for Development of Advanced Computing (CDAC), Mumbai, India.
Centre for Development of Advanced Computing (CDAC), Mumbai, India.
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
Show others and affiliations
2016 (English)In: 2016 INTERNATIONAL CONFERENCE ON BIOMETRICS (ICB) / [ed] J. Fierrez, S.Z. Li, A. Ross, R. Veldhuis, F. Alonso-Fernandez, J. Bigun, Piscataway: IEEE, 2016Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents a state-of-the-art iris segmentation framework specifically for non-ideal irises. The framework adopts coarse-to-fine strategy to localize different boundaries. In the approach, pupil is coarsely detected using an iterative search method exploiting dynamic thresholding and multiple local cues. The limbic boundary is first approximated in polar space using adaptive filters and then refined in Cartesianspace. The framework is quite robust and unlike the previously reported works, does notrequire tuning of parameters for different databases. The segmentation accuracy (SA) is evaluated using well known measures; precision, recall and F-measure, using the publicly available ground truth data for challenging iris databases; CASIAV4-Interval, ND-IRIS-0405, and IITD. In addition, the approach is also evaluated on highly challenging periocular images of FOCS database. The validity of proposed framework is also ascertained by providing comprehensive comparisons with classical approaches as well asstate-of-the-art methods such as; CAHT, WAHET, IFFP, GST and Osiris v4.1. The results demonstrate that our approach provides significant improvements in segmentation accuracy as well as in recognition performance that too with lower computational complexity. © 2016 IEEE.

Place, publisher, year, edition, pages
Piscataway: IEEE, 2016.
Series
International Conference on Biometrics, ISSN 2376-4201
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:hh:diva-31745DOI: 10.1109/ICB.2016.7550096ISI: 000390841200050Scopus ID: 2-s2.0-84988372923ISBN: 978-1-5090-1869-7 (print)OAI: oai:DiVA.org:hh-31745DiVA: diva2:952046
Conference
9th IAPR International Conference on Biometrics, Halmstad, Sweden, June 13-16, 2016
Funder
Swedish Research Council
Available from: 2016-08-11 Created: 2016-08-11 Last updated: 2017-12-01Bibliographically approved

Open Access in DiVA

fulltext(1556 kB)321 downloads
File information
File name FULLTEXT01.pdfFile size 1556 kBChecksum SHA-512
9567e4e8ced2c47841e9bd023db3cc224806c00bb36d90322319b87042f2542ab2dc904601fd5baf53381c6a4888221a901ebab76b3057d5b0399d524135e2bf
Type fulltextMimetype application/pdf

Other links

Publisher's full textScopus

Authority records BETA

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
Total: 321 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 140 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