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Best Regions for Periocular Recognition with NIR and Visible Images
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
Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).ORCID-id: 0000-0002-4929-1262
2014 (engelsk)Inngår i: 2014 IEEE International Conference on Image Processing (ICIP), Piscataway, NJ: IEEE Press, 2014, s. 4987-4991Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

We evaluate the most useful regions for periocular recognition. For this purpose, we employ our periocular algorithm based on retinotopic sampling grids and Gabor analysis of the spectrum. We use both NIR and visible iris images. The best regions are selected via Sequential Forward Floating Selection (SFFS). The iris neighborhood (including sclera and eyelashes) is found as the best region with NIR data, while the surrounding skin texture (which is over-illuminated in NIR images) is the most discriminative region in visible range. To the best of our knowledge, only one work in the literature has evaluated the influence of different regions in the performance of periocular recognition algorithms. Our results are in the same line, despite the use of completely different matchers. We also evaluate an iris texture matcher, providing fusion results with our periocular system as well. © 2014 IEEE.

sted, utgiver, år, opplag, sider
Piscataway, NJ: IEEE Press, 2014. s. 4987-4991
Emneord [en]
Biometrics, periocular, eye, Gabor filters
HSV kategori
Identifikatorer
URN: urn:nbn:se:hh:diva-25468DOI: 10.1109/ICIP.2014.7026010ISI: 000370063605031Scopus ID: 2-s2.0-84949926598ISBN: 978-1-4799-5751-4 (digital)OAI: oai:DiVA.org:hh-25468DiVA, id: diva2:720821
Konferanse
IEEE International Conference on Image Processing, ICIP, Paris, France, 27-30 October, 2014
Prosjekter
BBfor2
Forskningsfinansiär
Swedish Research Council, 2012-4313
Merknad

Article number: 7026010

Tilgjengelig fra: 2014-06-02 Laget: 2014-06-02 Sist oppdatert: 2018-03-22bibliografisk kontrollert

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