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Iris Classification Based on Its Quality

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Intelligent Computing Theories (ICIC 2013)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 7995))

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Abstract

This paper proposes an iris classification method based on the iris image quality. Quality of an iris image is modeled as a function of the attributes like focus, motion blur, occlusion, contrast and illumination, specular reflection and dilation. Values of these attributes are combined using a support vector machine (SVM) to provide the overall quality class of the image.

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References

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Indian Institute of Technology Kanpur, 208016, Kanpur, India

    Aditya Nigam, Anvesh T. & Phalguni Gupta

Authors
  1. Aditya Nigam
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  2. Anvesh T.
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  3. Phalguni Gupta
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Editor information

Editors and Affiliations

  1. Machine Learning and Systems Biology Laboratory, Tongji University, 4800 Caoan Road, 201804, Shanghai, China

    De-Shuang Huang

  2. Electrical and Electronics Department, Polytechnic of Bari, Via Orabona 4, 70125, Bari, Italy

    Vitoantonio Bevilacqua

  3. Faculty of Engineering, District University Francisco José de Caldas, Cra. 7a No. 40-53, Fifth Floor, Bogotá, Colombia

    Juan Carlos Figueroa

  4. School of Electrical, Computer and Telecommunications Engineering, The University of Wollongong, 2522, North Wollongong, NSW, Australia

    Prashan Premaratne

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Nigam, A., T., A., Gupta, P. (2013). Iris Classification Based on Its Quality. In: Huang, DS., Bevilacqua, V., Figueroa, J.C., Premaratne, P. (eds) Intelligent Computing Theories. ICIC 2013. Lecture Notes in Computer Science, vol 7995. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39479-9_53

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  • DOI: https://doi.org/10.1007/978-3-642-39479-9_53

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-39478-2

  • Online ISBN: 978-3-642-39479-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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