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Feature Extraction Using Class-Augmented Principal Component Analysis (CA-PCA)

  • Myoung Soo Park
  • Jin Hee Na
  • Jin Young Choi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4132)

Abstract

In this paper, we propose a novel feature extraction method called Class-Augmented PCA (CA-PCA) which uses class information. The class information is augmented to data and influences the extraction of features so that the features become more appropriate for classification than those from original PCA. Compared to other supervised feature extraction methods LDA and its variants, this scheme does not use the scatter matrix including inversion and therefore it is free from the problems of LDA originated from this matrix inversion. The performance of the proposed scheme is evaluated by experiments using two well-known face database and as a result we can show that the performance of the proposed CA-PCA is superior to those of other methods.

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References

  1. 1.
    Turk, M., Pentland, A.: Eigenfaces for recognition. Journal of Cognitive Neuroscience 3, 71–86 (1991)CrossRefGoogle Scholar
  2. 2.
    Duda, R., Hard, P.: Pattern Classification and Scene Analysis. Wiley, New York (1973)zbMATHGoogle Scholar
  3. 3.
    Fisher, R.A.: The Use of Multiple Measures in Taxonomic Problems. Annual Eugenics 7, 179–188 (1936)Google Scholar
  4. 4.
    Huang, R., Liu, Q., Lu, H., Ma, S.: Solving the Small Sample Size Problem of LDA. In: Proceedings of IEEE International Conf. on Pattern Recognition, vol. 3 (2002)Google Scholar
  5. 5.
    Belhumeur, P., Hespanha, J., Kriegman, D.: Eigenfaces vs. FisherFaces: Recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(7), 711–720 (1997)CrossRefGoogle Scholar
  6. 6.
    Yang, J., Yu, Y., Kunz, W.: An Efficient LDA Algorithm for Face Recognition. In: Proceedings of the 6th International Conference on Control, Automation, Robotics and Vision, ICARCV 2000 (2000)Google Scholar
  7. 7.
    Park, C.H., Park, H., Pardalos, P.: A Comparative Study of Linear and Nonlinear Feature Extraction Methods. In: Proceedings of 4th IEEE International Conference on Data Mining (ICDM 2004), pp. 495–498 (2004)Google Scholar
  8. 8.
    Yang, M.H.: Kernel Eigenfaces vs. Kernel Fisherfaces: Face Recognition Using Kernel Methods. In: Proceedings of 5th IEEE Internationl Conference on Automatic Face and Gesture Recognition (RGR 2002), pp. 215–220 (2002)Google Scholar
  9. 9.
    Yang, J., Zhang, D., Frangi, A.F., Yang, J.-y.: Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(1), 131–137 (2005)CrossRefGoogle Scholar
  10. 10.
    Meng, J.E., Chen, W., Shiqian, W.: Highspeed face recognition based on discrete cosine transform and RBF neural networks. IEEE Transactions on Neural Networks 16(3), 679–691 (2005)CrossRefGoogle Scholar
  11. 11.
    Kwak, N., Choi, C.-H., Ahuja, N.: Face recognition using feature extraction based on independent component analysis. In: Proceedings of International Conference on Image Processing 2002, Rochester (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Myoung Soo Park
    • 1
  • Jin Hee Na
    • 1
  • Jin Young Choi
    • 1
  1. 1.School of Electrical Engineering and Computer Science, ASRISeoul National UniversitySeoulKorea

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