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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.
Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).ORCID-id: 0000-0002-1400-346X
Vise andre og tillknytning
2016 (engelsk)Inngår i: 2016 INTERNATIONAL CONFERENCE ON BIOMETRICS (ICB) / [ed] J. Fierrez, S.Z. Li, A. Ross, R. Veldhuis, F. Alonso-Fernandez, J. Bigun, Piscataway: IEEE, 2016Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
Piscataway: IEEE, 2016.
Serie
International Conference on Biometrics, ISSN 2376-4201
HSV kategori
Identifikatorer
URN: urn:nbn:se:hh:diva-31745DOI: 10.1109/ICB.2016.7550096ISI: 000390841200050Scopus ID: 2-s2.0-84988372923ISBN: 978-1-5090-1869-7 (tryckt)OAI: oai:DiVA.org:hh-31745DiVA, id: diva2:952046
Konferanse
9th IAPR International Conference on Biometrics, Halmstad, Sweden, June 13-16, 2016
Forskningsfinansiär
Swedish Research CouncilTilgjengelig fra: 2016-08-11 Laget: 2016-08-11 Sist oppdatert: 2017-12-01bibliografisk kontrollert

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