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Exploting Periocular and RGB Information in Fake Iris Detection
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
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
2014 (English)In: 2014 37th International Conventionon Information and Communication Technology, Electronics and Microelectronics (MIPRO): 26 – 30 May 2014 Opatija, Croatia: Proceedings / [ed] Petar Biljanovic, Zeljko Butkovic, Karolj Skala, Stjepan Golubic, Marina Cicin-Sain, Vlado Sruk, Slobodan Ribaric, Stjepan Gros, Boris Vrdoljak, Mladen Mauher & Goran Cetusic, Rijeka: Croatian Society for Information and Communication Technology, Electronics and Microelectronics - MIPRO , 2014, 1354-1359 p.Conference paper, (Refereed)
Abstract [en]

Fake iris detection has been studied by several researchers. However, to date, the experimental setup has been limited to near-infrared (NIR) sensors, which provide grey-scale images. This work makes use of images captured in visible range with color (RGB) information. We employ Gray-Level CoOccurrence textural features and SVM classifiers for the task of fake iris detection. The best features are selected with the Sequential Forward Floating Selection (SFFS) algorithm. To the best of our knowledge, this is the first work evaluating spoofing attack using color iris images in visible range. Our results demonstrate that the use of features from the three color channels clearly outperform the accuracy obtained from the luminance (gray scale) image. Also, the R channel is found to be the best individual channel. Lastly, we analyze the effect of extracting features from selected (eye or periocular) regions only. The best performance is obtained when GLCM features are extracted from the whole image, highlighting that both the iris and the surrounding periocular region are relevant for fake iris detection. An added advantage is that no accurate iris segmentation is needed. This work is relevant due to the increasing prevalence of more relaxed scenarios where iris acquisition using NIR light is unfeasible (e.g. distant acquisition or mobile devices), which are putting high pressure in the development of algorithms capable of working with visible light. © 2014 MIPRO.

Place, publisher, year, edition, pages
Rijeka: Croatian Society for Information and Communication Technology, Electronics and Microelectronics - MIPRO , 2014. 1354-1359 p.
Keyword [en]
Feature extraction, Iris recognition, Iris, Image color analysis, Support vector machines, Databases, Accuracy
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:hh:diva-25120DOI: 10.1109/MIPRO.2014.6859778ISI: 000346438700258Scopus ID: 2-s2.0-84906920702ISBN: 978-953-233-081-6 (print)OAI: oai:DiVA.org:hh-25120DiVA: diva2:713046
Conference
37th International Convention on Information and Communication Technology, Electronics and Microelectronics, MIPRO, Special Session on Biometrics & Forensics & De-identification and Privacy Protection, BiForD, Opatija, Croatia, 26-30th May, 2014
Projects
BBfor2
Funder
Swedish Research Council, 2012-4313
Note

Article number: 6859778; F. A.-F. thanks the Swedish Research Council and the EU for funding his postdoctoral work. Authors acknowledge the CAISR program of the Swedish Knowledge Foundation, the EU BBfor2 project and the EU COST Action IC1106.

Available from: 2014-04-17 Created: 2014-04-17 Last updated: 2017-03-27Bibliographically approved

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CiteExportLink to record
Permanent link

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Citation style
  • apa
  • harvard1
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Output format
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