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The Adaptive Authentication System for Behavior Biometrics Using Pareto Learning Self Organizing Maps

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Neural Information Processing. Models and Applications (ICONIP 2010)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6444))

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Abstract

In this paper, we propose an authentication system which can adapt to the temporal changes of the behavior biometrics with accustoming to the system. We proposed the multi-modal authentication system using Supervised Pareto learning Self Organizing Maps. In this paper, the adaptive authentication system with incremental learning which is applied as the feature of neural networks is developed.

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References

  1. Bolle, R., Connell, J., Pankanti, S., Ratha, N., Senior, A.: Guide to Biometrics. Springer, Heidelberg (2004)

    Book  Google Scholar 

  2. Monrose, F., Rubin, A.D.: Keystroke Dynamics as a Biometric for Authentication. Future Generation Computer Systems (March 2000)

    Google Scholar 

  3. Dokic, S., Kulesh, A., et al.: An Overview of Multi-modal Biometrics for Authentication. In: Proceedings of The 2007 International Conference on Security and Management, pp. 39–44 (2007)

    Google Scholar 

  4. Kohonen, T.: Self Organizing Maps. Springer, Heidelberg, ISBN 3-540-67921-9

    Google Scholar 

  5. Nakakuni, M., Dozono, H., et al.: Application of Self Organizing Maps for the Integrated Authentication using Keystroke Timings and Handwritten Symbols. Wseas Transactions On Information Science & Applications 2-4, 413–420 (2007)

    Google Scholar 

  6. Dozono, H., Nakakuni, M.: An Integration Method of Multi-Modal Biometrics Using Supervised Pareto Learning Self Organizing Maps. In: Proceedings of 2008 International Joint Conference on Neural Networks, pp. 603–607. IEEE, Los Alamitos (2008)

    Google Scholar 

  7. Dozono, H., Nakakuni, M.: Application of Supervised Pareto Learning Self Organizing Maps amd Its Incremental Learning. In: Advances in Self Organizing Maps. LNCS, vol. 5629, pp. 54–62. Springer, Heidelberg (2009)

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  8. Dozono, H., Nakakuni, M.: Analysis of robustness of pareto learning SOM to variances of input vectors. In: Chan, J.H. (ed.) ICONIP 2009, Part II. LNCS, vol. 5864, pp. 836–844. Springer, Heidelberg (2009)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Faculty of Science and Engineering, Saga University, 1-Honjyo, Saga, 840-8502, Japan

    Hiroshi Dozono, Shinsuke Itou & Shigeomi Hara

  2. Information Technology Center, Fukuoka University, 8-19-1, Nanakuma, Jonan-ku, Fukuoka, 814-0180, Japan

    Masanori Nakakuni

Authors
  1. Hiroshi Dozono
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  2. Masanori Nakakuni
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  3. Shinsuke Itou
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  4. Shigeomi Hara
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Editor information

Editors and Affiliations

  1. School of Information Technology, Murdoch University, 6150, Murdoch, WA, Australia

    Kok Wai Wong

  2. The Australian National University, 0200, Canberra, ACT, Australia

    B. Sumudu U. Mendis

  3. School of Electrical, Computer and Telecommunications Engineering, University of Wollongong, Northfields Avenue, 2522, P.O. Box, Wollongong, NSW, Australia

    Abdesselam Bouzerdoum

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© 2010 Springer-Verlag Berlin Heidelberg

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Dozono, H., Nakakuni, M., Itou, S., Hara, S. (2010). The Adaptive Authentication System for Behavior Biometrics Using Pareto Learning Self Organizing Maps. In: Wong, K.W., Mendis, B.S.U., Bouzerdoum, A. (eds) Neural Information Processing. Models and Applications. ICONIP 2010. Lecture Notes in Computer Science, vol 6444. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17534-3_47

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  • DOI: https://doi.org/10.1007/978-3-642-17534-3_47

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-17533-6

  • Online ISBN: 978-3-642-17534-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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