Advertisement

The Application of Extended Geodesic Distance in Head Poses Estimation

  • Bingpeng Ma
  • Fei Yang
  • Wen Gao
  • Baochang Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3832)

Abstract

This paper we proposes an extended geodesic distance for head pose estimation. In ISOMAP, two approaches are applied for neighborhood construction, called k-neighbor and ε-neighbor. For the k-neighbor, the number of the neighbors is a const k. For the other one, all the distances between the neighbors is less than ε. Either the k-neighbor or the ε-neighbor neglects the difference of each point. This paper proposes an new method called the kc-neighbor, in which the neighbors are defined based on c time distance of the k nearest neighbor, which can avoid the neighborhood graph unconnected and improve the accuracy in computing neighbors. In this paper, SVM rather than MDS is applied to classify head poses after the geodesic distances are computed. The experiments show the effectiveness of the proposed method.

Keywords

Linear Discriminant Analysis Face Image Geodesic Distance Neighborhood Graph Dimensionality Reduction Method 
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.

References

  1. 1.
    Turk, M., Pentland, A.: Eigenfaces for Recognition. Journal of Cognitive Neuroscience (3), 71–86 (1991)CrossRefGoogle Scholar
  2. 2.
    Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. Fisherfaces: Recognition using class specfic linear projection. IEEE Trans. on PAMI 19(7), 711–720 (1997)Google Scholar
  3. 3.
    Bartlett, M.S., Sejnowski, T.J.: Independent components of face images: A representation for face recognition. In: Proceedings of the 4th Annual Jount Symposium on Neural Computation, Pasadena, CA, May 17 (1997)Google Scholar
  4. 4.
    M.-H. Yang: Extended Isomap for Classification. ICPR (3), 615–618 (2002)Google Scholar
  5. 5.
    Tenenbaum, J.B., de Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290, 2319–2323 (2000)CrossRefGoogle Scholar
  6. 6.
    Roweis, S., Saul, L.: Nonlinear dimensionality reduction by locally linear embedding. Science 290, 2323–2326 (2000)CrossRefGoogle Scholar
  7. 7.
    Belkin, M., Niyogi, P.: Laplacian eigenmaps and spetral techniques for embedding and clustering. Advances in Neural Information Processing Systems 15 (2001)Google Scholar
  8. 8.
    Cox, T.F., Cox, M.A.A.: Multidimensional Scaling. CRC Press, Boca Raton (2000)CrossRefGoogle Scholar
  9. 9.
    Cortes, C., Vapnik, V.: Support vector network. Machine Learning 20, 273–297 (1995)MATHGoogle Scholar
  10. 10.
    Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines (2001), Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
  11. 11.
    Phillips, P.J., Moon, H., et al.: The FERET evaluation methodology for face -recognition algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(10), 1090–1104 (2000)CrossRefGoogle Scholar
  12. 12.
    Gao, W., Cao, B., Shan, S., Zhang, X., Zhou, D.: The CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations, technical report of JDL (2004), http://www.jdl.ac.cn/~peal/peal_tr.pdf

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Bingpeng Ma
    • 1
    • 3
  • Fei Yang
    • 1
    • 3
  • Wen Gao
    • 1
    • 2
    • 3
  • Baochang Zhang
    • 2
  1. 1.Institute of Computing TechnologyChinese Academy of SciencesBeijingChina
  2. 2.Department of Computer Science and EngineeringHarbin Institute of TechnologyHarbinChina
  3. 3.Graduate School of the Chinese Academy of SciencesBeijingChina

Personalised recommendations