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FLD-SIFT: Class Based Scale Invariant Feature Transform for Accurate Classification of Faces

  • B. H. Shekar
  • M. Sharmila Kumari
  • Leonid M. Mestetskiy
  • Natalia Dyshkant
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 142)

Abstract

In this paper, a new model called FLD-SIFT is devised for compact representation and accurate recognition of faces. Unlike scale invariant feature transform model that uses smoothed weighted histogram and massive dimension of feature vectors, in the proposed model, an image patch centered around the keypoint has been considered and linear discriminant analysis (FLD) is employed for compact representation of image patches. Contrasting to PCA-SIFT model that employs principal component analysis (PCA) on a normalized gradient patch, we employ FLD on an image patch exists around the keypoints. The proposed model has better computing performance in terms of recognition time than the basic SIFT model. To establish the superiority of the proposed model, we have experimentally compared the performance of our new algorithm with (2D)2-PCA, (2D)2-FLD and basic SIFT model on the AT&T face database.

Keywords

Linear discriminant analysis Local descriptor Face classification 

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References

  1. 1.
    Jain, A.K., Ross, A., Prabhakar, S.: An introduction to biometric recognition. IEEE Transactions on Circuits and Systems for Video Technology 14(1), 4–20 (2004)CrossRefGoogle Scholar
  2. 2.
    Harris, C., Stephens, M.: A combined corner and edge detector. In: Alvey Vision Conference, pp. 147–151 (1988)Google Scholar
  3. 3.
    Zhang, D., Zhou, Z.-H. (2D)2PCA: 2-directional 2-dimensional PCA for efficient face representation and recognition. Journal of Neurocomputing 69(1-3), 224–231 (2005)CrossRefGoogle Scholar
  4. 4.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  5. 5.
    Xiong, H., Swamy, M.N.S., Ahmad, M.O.: Two-dimensional FLD for face recognition. Pattern Recognition 38(7), 1121–1124 (2005)CrossRefGoogle Scholar
  6. 6.
    Koenderink, J., van Doorn, A.: Representation of local geometry in the visual system. Biological Cybernetics 55, 367–375 (1987)MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    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 26(1), 131–137 (2004)CrossRefGoogle Scholar
  8. 8.
    Yang, J., Zhang, D., Yang, X., Yang, J.: Two-dimensional discriminant transform for face recognition. Pattern Recognition 38(7), 1125–1129 (2005)CrossRefzbMATHGoogle Scholar
  9. 9.
    Sirovich, L., Kirby, M.: Low dimensional procedure for the characterization of human faces. Journal of the Optical Society of America A: Optics, Image science, and Vision 4(3), 519–524 (1987)CrossRefGoogle Scholar
  10. 10.
    Van Gool, L., Moons, T., Ungureanu, D.: Affine/photometric invariants for planar intensity patterns. In: Buxton, B.F., Cipolla, R. (eds.) ECCV 1996. LNCS, vol. 1065. Springer, Heidelberg (1996)Google Scholar
  11. 11.
    Kirby, M., Sirovich, L.: Application of the Karhunen-Loeve procedure for the characterization of human faces. IEEE Transactions on Pattern Analysis and Machine Intelligence 12(1), 103–107 (1990)CrossRefGoogle Scholar
  12. 12.
    Li, M., Yuan, B.: 2D-LDA: A statistical linear discriminant analysis for image matrix. Pattern Recognition Letters 26(5), 527–532 (2005)CrossRefGoogle Scholar
  13. 13.
    Turk, M., Pentland, A.: Eigenfaces for recognition. Journal of Cognitive Neuroscience 3(1), 71–86 (1991)CrossRefGoogle Scholar
  14. 14.
    Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. In: Proceedings of Computer Vision and Pattern Recognition (June 2003)Google Scholar
  15. 15.
    Mikolajczyk, K., Schmid, C.: Indexing based on scale invariant interest points. In: Proceedings of International Conference on Computer Vision, pp. 525–531 (July 2001)Google Scholar
  16. 16.
    Nagabhushan, P., Guru, D.S., Shekar, B.H. (2D)2 FLD: An efficient approach for appearance based object recognition. Journal of Neurocomputing 69(7-9), 934–940 (2006)CrossRefGoogle Scholar
  17. 17.
    Belhumeur, P.N., Hespanha, J.P., Kreigman, 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
  18. 18.
    Zhao, W., Chellappa, R., Phillips, P.J., Rosenfeld, A.: Face Recognition: A Literature Survey. ACM Computing Survey 35(4), 399–458 (2003)CrossRefGoogle Scholar
  19. 19.
    Ke, Y., Sukthankar, R.: PCA-SIFT: A more distinctive representation for local image descriptors. In: Computer Vision and Pattern Recognition (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • B. H. Shekar
    • 1
  • M. Sharmila Kumari
    • 2
  • Leonid M. Mestetskiy
    • 3
  • Natalia Dyshkant
    • 3
  1. 1.Department of Computer ScienceMangalore UniversityIndia
  2. 2.Department of Computer Science and EngineeringP A College of EngineeringMangaloreIndia
  3. 3.Department of Computational Mathematics and CyberneticsMoscow State UniversityMoscowRussia

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