Advertisement

Face Recognition Using MPCA-EMFDA Based Features Under Illumination and Expression Variations in Effect of Different Classifiers

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 264)

Abstract

The paper proposes a new method for feature extraction using tensor based Each Mode Fisher Discriminant Analysis(EMFDA) over Multilinear Principle Components (MPCA) in effect of different classifiers while changing feature size. Initially the face datasets have been mapped into curvilinear tensor space and features have been extracted using Multilinear Principal Component Analysis (MPCA) followed by Fisher Discriminant Analysis, in each mode of tensor space. The ORL and YALE databases have been used, without any pre-processing, in order to test the effect of classifier in real time environment.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Zhao, W., Chellappa, R., Phillips, P.J., Rosenfeld, A.: Face recognition: A literature survey. ACM Computing Surveys (CSUR) 35(4), 399–458 (2003)CrossRefGoogle Scholar
  2. 2.
    Turk, M., Pentland, A.: Eigenfaces for recognition. Journal of Cognitive Neuroscience 3(1), 71–86 (1991)CrossRefGoogle Scholar
  3. 3.
    Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(7), 711–720 (1997)CrossRefGoogle Scholar
  4. 4.
    Zhao, W., Krishnaswamy, A., Chellappa, R., Swets, D.L., Weng, J.: Discriminant analysis of principal components for face recognition. In: Face Recognition, pp. 73–85. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  5. 5.
    Singh, K.P., Kumar, M., Tripathi, C.: Face Recognition using Eigen Tensor based Linear Discriminant Analysis with SVM Classifier, M.Tech Thesis, Indian Institute of Information Technology Allahabad (2011)Google Scholar
  6. 6.
    Mika, S., Ratsch, G., Weston, J., Scholkopf, B., Mullers, K.R.: Fisher discriminant analysis with kernels. In: Proceedings of the 1999 IEEE Signal Processing Society Workshop on Neural Networks for Signal Processing IX, pp. 41–48. IEEE (August 1999)Google Scholar
  7. 7.
    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 (2004)CrossRefGoogle Scholar
  8. 8.
    Yu, H., Bennamoun, M.: 1D-PCA, 2D-PCA to nD-PCA. In: 18th International Conference on Pattern Recognition, ICPR 2006, vol. 4, pp. 181–184. IEEE (August 2006)Google Scholar
  9. 9.
    Lu, H., Plataniotis, K.N., Venetsanopoulos, A.N.: Multilinear principal component analysis of tensor objects for recognition. In: 18th International Conference on Pattern Recognition, ICPR 2006, vol. 2, pp. 776–779. IEEE (August 2006)Google Scholar
  10. 10.
    Xu, D., Yan, S., Zhang, L., Zhang, H.J., Liu, Z., Shum, H.Y.: Concurrent subspaces analysis. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 2, pp. 203–208. IEEE (June 2005)Google Scholar
  11. 11.
    Lu, H., Plataniotis, K.N., Venetsanopoulos, A.N.: MPCA: Multilinear principal component analysis of tensor objects. IEEE Transactions on Neural Networks 19(1), 18–39 (2008)Google Scholar
  12. 12.
    Yan, S., Xu, D., Yang, Q., Zhang, L., Tang, X., Zhang, H.J.: Discriminant analysis with tensor representation. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 1, pp. 526–532. IEEE (June 2005)Google Scholar
  13. 13.
    Wang, J., Plataniotis, K.N., Venetsanopoulos, A.N.: Selecting discriminant eigenfaces for face recognition. Pattern Recognition Letters 26(10), 1470–1482 (2005)CrossRefGoogle Scholar
  14. 14.
    Gates, K.E.: Fast and accurate face recognition using support vector machines. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition-Workshops, CVPR Workshops, p. 163. IEEE (June 2005)Google Scholar
  15. 15.
    Gold, C., Sollich, P.: Model selection for support vector machine classification. Neurocomputing 55(1), 221–249 (2003)CrossRefGoogle Scholar
  16. 16.
    Tripathi, C., Singh, K.P.: A new method for face recognition with fewer features under illumination and expression variations. In: 2012 19th International Conference on High Performance Computing (HiPC), pp. 1–9. IEEE (December 2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of Computer ScienceVKPKanpurIndia

Personalised recommendations