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Subspace Algorithms for Face Verification

  • Maciej Smiatacz
Conference paper
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 95)

Abstract

In real-life applications of face recognition the task of verification appears to be more important than classification. Usually we only have a limited collection of training images of some person and we want to decide if the acquired photograph is similar enough to them without using a separate set of negative samples. In this case it seems reasonable to use a subspace method based on a coordinate system constructed individually for the given class (person). This work presents two such methods: one based on SDF and the other inspired by Clafic algorithm. In the experimental section they are compared to the two-class SVM on the realistic data set taken from CMU-PIE database. The results confirm the advantages of subspace approach.

Keywords

Face Recognition Training Image Subspace Method False Acceptance Rate False Rejection Rate 
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.
    Phillips, P.J., Scruggs, W.T., O’Toole, A.J., Flynn, P.J., Bowyer, K.W., Schott, C.L., Sharpe, M.: FRVT 2006, and ICE 2006, Large-Scale Results. NISTIR 7408 National Institute of Standards and Technology, Gaithersburg (2007)Google Scholar
  2. 2.
    Cheng, Y.-Q., Liu, K., Yang, J.-Y.: A Novel Feature Extraction Method for Image Recognition Based on Similar Discriminant Function (SDF). Pattern Recognition 26(1), 115–125 (1993)CrossRefGoogle Scholar
  3. 3.
    Cheng, Y.-Q., Zhuang, Y.-M., Yang, J.-Y.: Optimal Fisher Discriminant Analysis Using the Rank Decomposition. Pattern Recognition 25(1), 101–111 (1992)CrossRefMathSciNetGoogle Scholar
  4. 4.
    Smiatacz, M., Malina, W.: New variants of the SDF classifier. In: Kurzynski, M., Wozniak, M. (eds.) Computer Recognition Systems 3. AISC, vol. 57, pp. 205–212. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  5. 5.
    Watanabe, S., Lambert, P.F., Kulikowski, C.A., Buxton, J.L., Walker, R.: Evaluation and Selection of Variables in Pattern Recognition. Computer and Information Sciences II, 91–122 (1967)Google Scholar
  6. 6.
    Watanabe, S.: Karhunen-Loeve Expansion and Factor Analysis. In: Trans. Fourth Prague Conf. on Information Theory, Statistical Decision Functions, Random Processes, pp. 635–660 (1965)Google Scholar
  7. 7.
    Turk, M., Pentland, A.: Eigenfaces for Recognition. J. Cognitive Neuroscience 3(1), 71–86 (1990)CrossRefGoogle Scholar
  8. 8.
    Smiatacz, M., Malina, W.: Modifying the Input Data Structure for Fisher Classifier. In: Proc. Second Conf. on Computer Recognition Systems (KOSYR 2001), pp. 363–367 (2001)Google Scholar
  9. 9.
    Yang, J., Zhang, D., Frangi, A.F., Yang, J.Y.: Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition. IEEE Trans. Pattern Analysis and Machine Intelligence 26(1), 131–137 (2004)CrossRefGoogle Scholar
  10. 10.
    Sim, T., Baker, S., Bsat, M.: The CMU Pose, Illumination, and Expression (PIE) Database. In: Proc. 5th Int. Conf. on Automatic Face and Gesture Recognition (2002)Google Scholar
  11. 11.
    Viola, P., Jones, M.J.: Robust Real-Time Face Detection. Int. J. Comp. Vision 57(2), 137–154 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Maciej Smiatacz
    • 1
  1. 1.Faculty of Electronics, Telecommunications and InformaticsGdansk University of TechnologyGdanskPoland

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