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A Modified Large Margin Classifier in Hidden Space for Face Recognition

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Multimedia Content Representation, Classification and Security (MRCS 2006)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 4105))

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Abstract

Considering some limitations of the existing large margin classifier (LMC) and support vector machines (SVMs), this paper develops a modified linear projection classification algorithm based on the margin, termed modified large margin classifier in hidden space (MLMC). MLMC can seek a better classification hyperplane than LMC and SVMs through integrating the within-class variance into the objective function of LMC. Also, the kernel functions in MLMC are not required to satisfy the Mercer’s condition. Compared with SVMs, MLMC can use more kinds of kernel functions. Experiments on the FERET face database confirm the feasibility and effectiveness of the proposed method.

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References

  1. Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)

    MATH  Google Scholar 

  2. Freund, Y., Schapire, R.E.: Large margin classification using the perceptron algorithm. Machine Learning 37(3), 277–296 (1999)

    Article  MATH  Google Scholar 

  3. Huang, K., Yang, H., King, I.: Learning large margin classifiers locally and globally. In: Proceedings of the Twenty-First International Conference on Machine Learning, Banff, Alberta, Canada, vol. 69 (2004)

    Google Scholar 

  4. Hsu, C., Lin, C.: A Comparison of Methods for Multiclass Support Vector Machines. IEEE Transaction on Neural Networks 13(2), 415–425 (2002)

    Article  Google Scholar 

  5. Li, Z., Wei-Dai, Z., Li-Cheng, J.: Hidden space support vector machines. IEEE Transactions on Neural Networks 15(6), 1424–1434 (2004)

    Article  Google Scholar 

  6. Yun-peng, C.: Matrix theory. Northwest Industry University Press, Xi’an (1999) (in chinese)

    Google Scholar 

  7. Müller, K.-R., Mika, S., Rätsch, G., Tsuda, K., Schölkopf, B.: An introduction to kernel-based learning algorithms. IEEE Transactions on Neural Networks 12(2), 181–201 (2001)

    Article  Google Scholar 

  8. Phillips, P.J., Moon, H., Rizvi, S.A., Rauss, P.J.: The FERET Evaluation Methodology for Face-Recognition Algorithms. IEEE Trans. Pattern Anal. Machine Intell. 22(10), 1090–1104 (2000)

    Article  Google Scholar 

  9. Phillips, P.J.: The Facial Recognition Technology (FERET) Database, http://www.itl.nist.gov/iad/humanid/feret/feret_master.html

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© 2006 Springer-Verlag Berlin Heidelberg

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Chen, Ck., Peng, Qq., Yang, Jy. (2006). A Modified Large Margin Classifier in Hidden Space for Face Recognition. In: Gunsel, B., Jain, A.K., Tekalp, A.M., Sankur, B. (eds) Multimedia Content Representation, Classification and Security. MRCS 2006. Lecture Notes in Computer Science, vol 4105. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11848035_22

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  • DOI: https://doi.org/10.1007/11848035_22

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-39392-4

  • Online ISBN: 978-3-540-39393-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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