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International Conference on Information Systems Security

ICISS 2015: Information Systems Security pp 542–550Cite as

Robustness of Score Normalization in Multibiometric Systems

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Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 9478))

Abstract

This paper presents an evaluation of normalization techniques of matching scores on the recognition performance of a multibiometric system. We present two score normalization techniques, namely modified-linear-tanh-linear (MLTL) and four-segments-double-sigmoid (FSDS) that are found to be robust in achieving the recognition performance to the optimum value. The techniques are tested in fusion of the two face recognition methods Fisherface and A-LBP on the dataset of uncontrolled environments. In particular, AT & T (ORL) face dataset is used in this experiment. The performance of the MLTL and FSDS score normalization techniques are compared with the existing normalization techniques, for instance min-max, tanh and linear-tanh-linear (LTL). The proposed normalization techniques show the significant improvement in the recognition performance of the multibiometric system over the known techniques.

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Acknowledgements

The authors acknowledge the Institute of Engineering and Technology (IET), Lucknow, Uttar Pradesh Technical University (UPTU), Lucknow for their partial financial support to carry out this research under the Technical Education Quality Improvement Programme (TEQIP-II) grant.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Institute of Engineering and Technology, 226 021, Lucknow, India

    Radhey Shyam & Yogendra Narain Singh

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  1. Radhey Shyam
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  2. Yogendra Narain Singh
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Correspondence to Radhey Shyam .

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Editors and Affiliations

  1. Center for Secure Information Systems, Fairfax, Virginia, USA

    Sushil Jajoda

  2. Jadavpur University, Kolkata, India

    Chandan Mazumdar

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© 2015 Springer International Publishing Switzerland

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Shyam, R., Singh, Y.N. (2015). Robustness of Score Normalization in Multibiometric Systems. In: Jajoda, S., Mazumdar, C. (eds) Information Systems Security. ICISS 2015. Lecture Notes in Computer Science(), vol 9478. Springer, Cham. https://doi.org/10.1007/978-3-319-26961-0_33

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  • DOI: https://doi.org/10.1007/978-3-319-26961-0_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26960-3

  • Online ISBN: 978-3-319-26961-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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