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One-Pass Incremental Membership Authentication by Face Classification

  • Shaoning Pang
  • Seiichi Ozawa
  • Nikola Kasabov
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3072)

Abstract

Real membership authentication applications require machines to learn from stream data while making a decision as accurately as possible whenever the authentication is needed. To achieve that, we proposed a novel algorithm which authenticated membership by a one-pass incremental principle component analysis(IPCA) learning. It is demonstrated that the proposed algorithm involves an useful incremental feature construction in membership authentication, and the incremental learning system works optimally due to its performance is converging to the performance of a batch learning system.

Keywords

Face Image Incremental Learning Eigenvector Matrix Current Prototype Batch Learning 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Pang, S.N., Kim, D., Bang, S.Y.: Membership authentication in the dynamic group by face classification using SVM ensemble. Pattern Recognition Letters 24, 215–225 (2003)zbMATHCrossRefGoogle Scholar
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    Pang, S.N., Kim, D., Bang, S.Y.: Fraud detection using support vector machine ensemble. In: Proc. ICORNIP 2001, pp. 1344–1349 (2001)Google Scholar
  3. 3.
    Kasabov, N.: Evolving connectionist systems: Methods and applications in bioinformatics, brain study and intelligent machines. Springer, Heidelberg (2002)zbMATHGoogle Scholar
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    Hall, P., Martin, R.: Incremental eigenanalysis for classification. In: British Machine Vision Conference, vol. 1, pp. 286–295 (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Shaoning Pang
    • 1
  • Seiichi Ozawa
    • 2
  • Nikola Kasabov
    • 1
  1. 1.Knowledge Engineering & Discover Research InstituteAuckland University of TechnologyAucklandNew Zealand
  2. 2.Graduate School of Science and TechnologyKobe UniversityKobeJapan

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