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An Online Learning-Based Adaptive Biometric System

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Adaptive Biometric Systems

Part of the book series: Advances in Computer Vision and Pattern Recognition ((ACVPR))

Abstract

In the last decade, adaptive biometrics has become an emerging field of research. Considering the fact that limited work has been undertaken on adaptive biometrics using machine learning techniques, in this chapter we list and discuss a few out of many potential learning techniques that can be applied to build an adaptive biometric system. In order to illustrate the efficacy of one of the incremental learning techniques from the literature, we built an adaptive biometric system. For experimentation, we have used multi-modal ocular (sclera and iris) data. The preliminary results have been reported in the results section, which are very promising.

\(^*\)The first and the second author have equal contribution in this work.

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Authors and Affiliations

  1. Institute for Integrated and Intelligent Systems, Griffith University, Queensland, Australia

    A. Das, R. Kunwar & M. Blumenstein

  2. Computer Vision and Pattern Recognition Unit, Indian Statistical Institute, Kolkata, India

    U. Pal

  3. IDeTIC, University of Las Palmas de Gran Canaria, Las Palmas, Spain

    M. A. Ferrer

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  1. A. Das
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  2. R. Kunwar
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  3. U. Pal
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  4. M. A. Ferrer
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  5. M. Blumenstein
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Correspondence to A. Das .

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Editors and Affiliations

  1. Michigan State University, East Lansing, Michigan, USA

    Ajita Rattani

  2. University of Cagliari, Cagliari, Italy

    Fabio Roli

  3. ETS, Montréal, Québec, Canada

    Eric Granger

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Das, A., Kunwar, R., Pal, U., Ferrer, M.A., Blumenstein, M. (2015). An Online Learning-Based Adaptive Biometric System. In: Rattani, A., Roli, F., Granger, E. (eds) Adaptive Biometric Systems. Advances in Computer Vision and Pattern Recognition. Springer, Cham. https://doi.org/10.1007/978-3-319-24865-3_5

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  • DOI: https://doi.org/10.1007/978-3-319-24865-3_5

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