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Common Image Method(Null Space + 2DPCAs) for Face Recognition

  • Hae Jong Seo
  • Young Kyung Park
  • Joong Kyu Kim
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4179)

Abstract

In this paper, we present a new scheme called Common Image method for face recognition. Our method has a couple of advantages over the conventional face recognition algorithms; one is that it can deal with the Small Sample Size(SSS) problem in LDA, and the other one is that it can achieve a better performance than traditional PCA by seeking the optimal projection vectors from image covariance matrix in a recognition task. As opposed to traditional PCA-based methods and LDA-based methods which employ Euclidean distance, Common Image methods adopted Assemble Matrix Distance(AMD) and IMage Euclidean Distance(IMED), by which the overall recognition rate could be improved. To test the recognition performance, a series of experiments were performed on CMU PIE, YaleB, and FERET face databases. The test results with these databases show that our Common Image method performs better than Discriminative Common Vector and 2DPCA-based methods.

Keywords

Face Recognition Recognition Rate Principle Component Analysis Null Space Common Image 
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.
    Yang, J., Zhang, D., Frangi, A.F., Yang, J.: Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence (2004)Google Scholar
  2. 2.
    Turk, M., Pentland, A.: Eigenfaces for Recognition. Journal of Cognitive Neuroscience (1991)Google Scholar
  3. 3.
    Belhumeur, P.N., Hespanha, J., Kriegman, D.J.: Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection. IEEE Transactions Pattern Analysis and Machine Intelligence (1997)Google Scholar
  4. 4.
    Zhao, W.: Discriminant Component Analysis for Face Recognition. In: Int. Conf. on Pattern Recognition (2000)Google Scholar
  5. 5.
    Cevikalp, H., Neamtu, M., Wilkes, M., Barkana, A.: Discriminative Common Vectors for Face. IEEE Transactions Pattern Analysis and Machine Intelligence (2005)Google Scholar
  6. 6.
    Swets, D., Weng, J.: Using discriminant eigenfeatures for image retrieval. IEEE Transactions Pattern Analysis and Machine Intelligence 18, 831–836 (1996)CrossRefGoogle Scholar
  7. 7.
    Chen, L., Liao, H., Ko, M., Lin, J., Yu, G.: A new lda-based face recognition system which can solve the small sample size problem. Pattern Recognition (2000)Google Scholar
  8. 8.
    Yu, H., Yang, J.: A direct lda algorithm for high-dimensional data with application to face recognition. Pattern Recognition 34, 2067–2070 (2001)MATHCrossRefGoogle Scholar
  9. 9.
    Wang, X., Tang, X.: Random sampling lda for face recognition. In: IEEE International Conference on Computer Vision and Pattern Recognition (2004)Google Scholar
  10. 10.
    Wang, X., Tang, X.: Dual-space linear discriminat analysis for face recognition. In: IEEE International Conference on Computer Vision and Pattern Recognition (2004)Google Scholar
  11. 11.
    Wang, L., Zhang, Y., Feng, J.: On the Euclidean Distance of Images. IEEE Transactions on Pattern Analysis and Machine Intelligence 26, 4–13 (2004)CrossRefGoogle Scholar
  12. 12.
    Huang, R., Liu, Q.S., Lu, H.Q., Ma, S.D.: Solving the Small Sample Size Problem of LDA. In: Proceeding IEEE ICPR (2002)Google Scholar
  13. 13.
    Sim, T., Baker, S., Bsat, M.: The CMU Pose, Illumination, and Expression Database. IEEE Transactions on Pattern Analysis and Machine Intelligence 25, 1615–1618 (2003)CrossRefGoogle Scholar
  14. 14.
    Zuo, W., Wang, K., Zhang, D.: Bi-Dierectional PCA with Assembled Matrix Distance Metric. In: IEEE ICIP 2005, vol. 2, pp. 958–961 (2005)Google Scholar
  15. 15.
    Georghiades, A.S., Belhumeur, P.N., Kriegman, D.J.: From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose. IEEE Transactions on Pattern Analysis and Machine Intelligence (2001)Google Scholar
  16. 16.
    Phillips, P.J., Moon, H., Rauss, P., Rizvi, S.A.: The FERET Evaluation Methodology for Face-Recognition Algorithms. In: Proceedings of Computer Vision and Pattern Recognition, Puerto Rico, pp. 137–143 (1997)Google Scholar
  17. 17.
    Kong, H., Xuchun, L., Wang, L., Teoh, E.K., Jian-Gang, W., Venkateswarlu, R.: Generalized 2D Principal Component Analysis. In: IEEE International Joint Conference on Neural Networks (IJCNN), Montreal, Canada (2005)Google Scholar
  18. 18.
    Zhang, D., Zhou, Z., Chen, S.: Diagonal Principal Component Analysis for Face Recognition. Pattern Recognition 39, 140–142 (2006)CrossRefGoogle Scholar
  19. 19.
    Daniel, J.J., Zia-ur, R., Glenn, A.W.: Properties and Performance of a Center/Surround Retinex. IEEE Transactions on Image Processing 6 (1997)Google Scholar
  20. 20.
    Fuknnaga, K.: Introduction to Statistical Pattern Recognition, 2nd edn. Academic Press, London (1991)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Hae Jong Seo
    • 1
  • Young Kyung Park
    • 1
  • Joong Kyu Kim
    • 1
  1. 1.School of Information and Communication Engineering, SKKUKyung-KiKorea

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