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Face Recognition Using Uncorrelated, Weighted Linear Discriminant Analysis

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Book cover Pattern Recognition and Image Analysis (ICAPR 2005)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 3687))

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Abstract

In this paper, we propose an uncorrelated, weighted LDA (UWLDA) technique for face recognition. The UWLDA extends the uncorrelated LDA (ULDA) technique by integrating the weighted pairwise Fisher criterion and nullspace LDA (NLDA), while retaining all merits of ULDA. Experiments compare the proposed algorithm to other face recognition methods that employ linear dimensionality reduction such as Eigenfaces, Fisherfaces, DLDA and NLDA on the AR face database. The results demonstrate the efficiency and superiority of our method.

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References

  1. Zhao, W., Chellappa, R., Rosenfeld, A., Phillips, P.J.: Face Recognition: A Literature Survey. ACM Computing Survey 35, 399–458 (2003)

    Article  Google Scholar 

  2. Jin, Z., Yang, J.Y., Hu, Z.S., Lou, Z.: Face Recognition Based on the Uncorrelated Discriminant Transformation. Pattern Recognition 34, 1405–1416 (2001)

    Article  MATH  Google Scholar 

  3. Chen, L.F., Liao, H.Y., Lin, J.C., Ko, M.T., Yu, G.J.: A New LDA-based Face Recognition System Which Can Solve the Small Sample Size Problem. Pattern Recognition 33, 1713–1726 (2000)

    Article  Google Scholar 

  4. Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. Fisherfaces: Recognitin Using Class Specific Linear Projection. IEEE Trans. Pattern Anal. Machine Intell. 9, 711–720 (1997)

    Article  Google Scholar 

  5. Loog, M., Duin, R.P.W., Haeb-Umbach, R.: Multiclass Linear Simension Reduction by Weighted Pairwise Fisher Criteria. IEEE Trans. Pattern Anal. Mach. Intell. 23, 762–766 (2001)

    Article  Google Scholar 

  6. Lotlikar, R., Kothari, R.: Fractional-Step Dimensionality Reduction. IEEE Trans. Pattern Anal. Mach. Intell. 22, 623–627 (2000)

    Article  Google Scholar 

  7. Huang, R., Liu, Q.S., Lu, H.Q., Ma, S.D.: Solving the Small Smaple Size Problem of LDA. In: Proceedings of the 16th International Conference on Pattern Recognition, vol. 3, pp. 29–32 (2002)

    Google Scholar 

  8. Martinez, A.M., Benavente, R.: The AR face database. CVC Tech. Report. 34 (1998)

    Google Scholar 

  9. Turk, M., Pentland, A.: Eigenfaces for Recognition. Journal of Cognitive Neuroscience 3, 72–86 (1991)

    Article  Google Scholar 

  10. Yu, H., Yang, J.: A Direct LDA Algorithm for High-dimensional Data – with Application to Face Recognition. Pattern Recognition 34, 2067–2070 (2001)

    Article  MATH  Google Scholar 

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© 2005 Springer-Verlag Berlin Heidelberg

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Liang, Y., Gong, W., Pan, Y., Li, W. (2005). Face Recognition Using Uncorrelated, Weighted Linear Discriminant Analysis. In: Singh, S., Singh, M., Apte, C., Perner, P. (eds) Pattern Recognition and Image Analysis. ICAPR 2005. Lecture Notes in Computer Science, vol 3687. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11552499_22

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  • DOI: https://doi.org/10.1007/11552499_22

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28833-6

  • Online ISBN: 978-3-540-31999-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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