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Fake Iris Detection: A Comparison Between Near-Infrared and Visible Images
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent Systems´ laboratory.ORCID iD: 0000-0002-1400-346X
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
2014 (English)In: Proceedings: 10th International Conference on Signal-Image Technology and Internet-Based Systems, SITIS 2014 / [ed] Kokou Yetongnon, Albert Dipanda & Richard Chbeir, Piscataway, NJ: IEEE Computer Society, 2014, 546-553 p.Conference paper, Published paper (Refereed)
Abstract [en]

Fake iris detection has been studied so far using near-infrared sensors (NIR), which provide grey scale-images, i.e. With luminance information only. Here, we incorporate into the analysis images captured in visible range, with color information, and perform comparative experiments between the two types of data. We employ Gray-Level Cocurrence textural features and SVM classifiers. These features analyze various image properties related with contrast, pixel regularity, and pixel co-occurrence statistics. We select the best features with the Sequential Forward Floating Selection (SFFS) algorithm. We also study the effect of extracting features from selected (eye or periocular) regions only. Our experiments are done with fake samples obtained from printed images, which are then presented to the same sensor than the real ones. Results show that fake images captured in NIR range are easier to detect than visible images (even if we down sample NIR images to equate the average size of the iris region between the two databases). We also observe that the best performance with both sensors can be obtained with features extracted from the whole image, showing that not only the eye region, but also the surrounding periocular texture is relevant for fake iris detection. An additional source of improvement with the visible sensor also comes from the use of the three RGB channels, in comparison with the luminance image only. A further analysis also reveals that some features are best suited to one particular sensor than the others. © 2014 IEEE

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE Computer Society, 2014. 546-553 p.
Keyword [en]
biometrics, attacks, fake iris, near-infrared iris, visible iris, GLCM features
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:hh:diva-26870DOI: 10.1109/SITIS.2014.104ISI: 000380564200080Scopus ID: 2-s2.0-84928540084ISBN: 978-1-4799-7978-3 (print)OAI: oai:DiVA.org:hh-26870DiVA: diva2:757570
Conference
Workshop on Insight on Eye Biometrics, IEB, in conjunction with the 10th International Conference on Signal-Image Technology and Internet-Based Systems, SITIS 2014, Marrakech, Morocco, 23-27 November, 2014
Funder
Swedish Research Council, 2012-4313Knowledge Foundation
Note

Article number: 7081596; Author F. A.-F. thanks the Swedish Research Council and the EU for for funding his postdoctoral research. Authors acknowledge the CAISR program of the Swedish Knowledge Foundation and the EU COST Action IC1106. Authors also thank the Biometric Recognition Group (ATVS-UAM) for making the ATVS-Flr database available.

Available from: 2014-10-22 Created: 2014-10-22 Last updated: 2017-09-27Bibliographically approved

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