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Face Recognition Based on Normalization and the Phase Spectrum of the Local Part of an Image

  • Jesus Olivares-Mercado
  • Kazuhiro Hotta
  • Haruhisa Takahashi
  • Hector Perez-Meana
  • Mariko Nakano Miyatake
  • Gabriel Sanchez-Perez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5359)

Abstract

This paper proposes a robust faces recognition method based on the Normalization and the Phase Spectrum of the Local Part of an Image. The Principal Components Analysis (PCA) and the Support Vector Machine (SVM) are used in the classification stage. We evaluate how the proposed method is robust to illumination, occlusion and expressions using ‘‘AR Face Database’’, which includes the face images of 109 subjects (60 males and 49 females) under illumination changes, expression changes and partial occlusion. The proposed method provides results with a correct recognition rate more than 96.7%.

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References

  1. 1.
    Reid, P.: BIOMETRICS for Networks Security, pp. 3–7. Prentice Hall, New Jersey (2004)Google Scholar
  2. 2.
    Chellappa, R., Wilson, C.L., Sirohey, S.: Human and machine recognition of faces: A survey. Proceedings of the IEEE 83(5), 705–740 (1995)CrossRefGoogle Scholar
  3. 3.
    Zhao, W., Chellappa, R., Phillips, P.J., Rosenfeld, A.: Face recognition: A literature survey. ACM Computing Surveys 35(4), 399–458 (2003)CrossRefGoogle Scholar
  4. 4.
    Aguilar-Torres, G., Sanchez-Perez, G., Nakano-Miyatake, M., Prez-Meana, H.: Face Recognition Algorithm Using the Discrete Gabor Transform. In: Conielecomp, p. 35 (2007)Google Scholar
  5. 5.
    Savvides, M., Vijaya Kumar, B.V.K., Khosla, P.K.: Eigenphases vs. Eigenfaces. In: Proceedings of the 17th International Conference on Pattern Recognition (ICPR 2004), August 2004, vol. 3 (2004)Google Scholar
  6. 6.
    Hotta, K.: Robust face recognition under partial occlusion based on support vector machine with local Gaussian summation kernel. Image an Vision Computing (in press, 2008)Google Scholar
  7. 7.
    Martinez, A.M.: Recognizing imprecisely localized partically occluded, an expression variant fece from a single sample per class. IEEE Trans. on Pattern Analysis and Machine Intelligence 24(6), 748–763 (2002)CrossRefGoogle Scholar
  8. 8.
    Martinez, A.M., Benavente, R.: The AR face database, CVC Technical Report # 24 (June 1998)Google Scholar
  9. 9.
    Smith, L.I.: A tutorial on Principal Components Analysis, February 26 (2002)Google Scholar
  10. 10.
    Bishop, C.M.: Pattern recognition and machine learning. Springer, Heidelberg (2006)zbMATHGoogle Scholar
  11. 11.
    Vapnik, V.N.: Statistical Learning Theory. John Eiley & sons (1998)Google Scholar
  12. 12.
    Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines (2001), Software available at: http://www.csie.ntu.edu.tw/~cjlin/libsvm
  13. 13.
    Hayes, M.H., Lim, J.S., Oppenheim, A.V.: Signal Reconstruction from Phase or Magnitude. IEEE Trans. Acoust., Signal Processing ASSP-28, 672–680 (1980)MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
    Oppenheim, A.V., Lim, J.S.: The importance of phase in signals. Proc. IEEE 69(5), 529–541 (1981)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Jesus Olivares-Mercado
    • 1
    • 2
  • Kazuhiro Hotta
    • 2
  • Haruhisa Takahashi
    • 2
  • Hector Perez-Meana
    • 1
  • Mariko Nakano Miyatake
    • 1
  • Gabriel Sanchez-Perez
    • 1
  1. 1.Instituto Politecnico NacionalESIME CulhuacanCol. San Francisco CulhuacanMexico
  2. 2.The University of Electro-CommunicationsTokyoJapan

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