Fingerprint Authentication Based on Matching Scores with Other Data

  • Koji Sakata
  • Takuji Maeda
  • Masahito Matsushita
  • Koichi Sasakawa
  • Hisashi Tamaki
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3832)

Abstract

A method of person authentication based on matching scores with the fingerprint data of others is proposed. Fingerprint data of others is prepared in advance as a set of representative data. Input fingerprint data is verified against the representative data, and the person belonging to the fingerprint is confirmed from the set of matching scores. The set of scores can be thought of as a feature vector, and is compared with the feature vector already enrolled. In this paper, the mechanism of the proposed method, the person authentication system using this method are described, and its advantage. Moreover, the simple criterion and selection method of the representative data are discussed. The basic performance when general techniques are used for the classifier is FNMR-3.6% at FMR-0.1%.

References

  1. 1.
    Ratha, N., Connell, J., Bolle, R.: Enhancing security and privacy in biometrics based authentication systems. IBM Systems Journal 40, 61–634 (2001)CrossRefGoogle Scholar
  2. 2.
    Soutar, C., Roberge, D., Stoianov, A., Gilroy, R., Kumar, V.: Biometric Encryption, http://www.bioscrypt.com/assets/Biometric_Encryption.pdf
  3. 3.
    Sasakawa, K., Isogai, F., Ikebata, S.: Personal Verification System with High Tolerance of Poor Quality Fingerprints. Proc. SPIE 1386, 265–272 (1990)CrossRefGoogle Scholar
  4. 4.
    Matsushita, M., Maeda, T., Sasakawa, K.: Personal verification using correlation of score sets calculated by standard biometrics data. Technical Paper of the Inst. of Electronics and Communication Engineers of Japan, PRMU 2000 78, 21–26 (2000)Google Scholar
  5. 5.
    Adler, A.: Sample images can be independently restored from face recognition template. In: Can. Conf. Electrical Computer Eng., pp. 1163–1166 (2003)Google Scholar
  6. 6.
    Breiman, L.: Bagging Predictors. Machine Learning 24(2), 123–140 (1996)MATHMathSciNetGoogle Scholar
  7. 7.
    Freund, Y., Schapire, R.E.: Experiments with a new boosting algorithm. In: Proc. of Thirteenth International Conference on Machine Learning, pp. 138–156 (1996)Google Scholar
  8. 8.
    Gama, J., Brazdil, P.: Cascade Generalization, Machine Learning, vol. 41(3), pp. 315–343. Kluwer Academic Publishers, Button (2000)Google Scholar
  9. 9.
    Wolpert, D.: Stacked Generalization. Neural Network 5(2), 241–260 (1992)CrossRefGoogle Scholar
  10. 10.
    Dzeroski, S., Zenko, B.: Is combining classifiers better than selecting the best one? Machine Learning 54, 255–273 (2004)MATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Koji Sakata
    • 1
  • Takuji Maeda
    • 1
  • Masahito Matsushita
    • 1
  • Koichi Sasakawa
    • 1
  • Hisashi Tamaki
    • 2
  1. 1.Advanced Technology R&D CenterMitsubishi Electric CorporationHyogoJapan
  2. 2.Faculty of EngineeringKobe UniversityHyogoJapan

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