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Sparse Representation for Ear Biometrics

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Advances in Visual Computing (ISVC 2008)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 5359))

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Abstract

In this paper we address for the first time, the problem of user identification using ear biometrics in the context of sparse representation. During the training session the compressed ear images are transformed to vectors to develop a dictionary matrix A [1]. The downsampled probe vector y is used to develop a linear, underdetermined system of equation y = Ax, x being unknown. The ill-posed system is regularized by utilizing the sparse nature of x and the inverse problem is solved through the l 1-norm minimization. Ideally the nonzero entries in the recovered vector x correspond to the class of the probe y. The developed system does not assume any preprocessing or normalization of the ear region. We did extensive experiments on the UND [2,3] and the FEUD [4] databases with session variability and incorporating different head rotations and lighting conditions. The proposed system is found to be robust under varying light and head rotations yielding a high recognition rate of the order of 98%. Moreover, in context of sparse representation a tuning parameter of the system is identified and is designated as an Operating Point (OP). The significance of the OP is highlighted by mathematical arguments and experimental verifications.

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Author information

Authors and Affiliations

  1. School of Electrical, Electronic and Computer Engineering, The University of Western Australia, Australia

    Imran Naseem & Roberto Togneri

  2. School of Computer Science and Software Engineering, The University of Western Australia, Australia

    Mohammed Bennamoun

Authors
  1. Imran Naseem
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  2. Roberto Togneri
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  3. Mohammed Bennamoun
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, University of Nevada, Reno, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, CA, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, NV, USA

    Darko Koracin

  5. Digital Image Research Center, Kingston University, London, UK

    Paolo Remagnino

  6. Mitsubishi Electric Research Laboratories, P.O. Box 02139, Cambridge, MA, USA

    Fatih Porikli

  7. Computer and Information Science and Engineering, University of Florida, P.O. Box, FL 32611-6120, Gainsville, USA

    Jörg Peters

  8. IBM T.J. Watson Research Center, 19 Skyline Drive, NY 10532, Hawthorne, USA

    James Klosowski

  9. 128 Memorial Mall, Stewart B001, IN 47907, West Lafayette, USA

    Laura Arns

  10. Denver Museum of Nature and Space, 2001 Colorade Blvd. Denver,, CO 80205, USA

    Yu Ka Chun

  11. Departmen of Computer Science,, NC State University, Campus Box 8206, NC 27695-8206, Raleigh, USA

    Theresa-Marie Rhyne

  12. Los Alamos National Labs, P.O. Box 1663, NM 87545, Los Alamos, USA

    Laura Monroe

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© 2008 Springer-Verlag Berlin Heidelberg

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Naseem, I., Togneri, R., Bennamoun, M. (2008). Sparse Representation for Ear Biometrics. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2008. Lecture Notes in Computer Science, vol 5359. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89646-3_33

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  • DOI: https://doi.org/10.1007/978-3-540-89646-3_33

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-89645-6

  • Online ISBN: 978-3-540-89646-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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