A New Distance Criterion for Face Recognition Using Image Sets

  • Tat-Jun Chin
  • David Suter
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3851)

Abstract

A major face recognition paradigm involves recognizing a person from a set of images instead of from a single image. Often, the image sets are acquired from a video stream by a camera surveillance system, or a combination of images which can be non-contiguous and unordered. An effective algorithm that tackles this problem involves fitting low-dimensional linear subspaces across the image sets and using a linear subspace as an approximation for the particular face identity. Unavoidably, the individual frames in the image set will be corrupted by noise and there is a degree of uncertainty on how accurate the resultant subspace approximates the set. Furthermore, when we compare two linear subspaces, how much of the distance between them is due to inter-personal differences and how much is due to intra-personal variations contributed by noise? Here, we propose a new distance criterion, developed based on a matrix perturbation theorem, for comparing two image sets that takes into account the uncertainty of estimating a linear subspace from noise affected image sets.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Lee, K., Ho, J., Yang, M., Kriegman, D.: Video-based face recognition using probabilistic appearance manifolds. In: IEEE Conf. on Computer Vision and Pattern Recognition (2003)Google Scholar
  2. 2.
    Shakhnarovich, G., Fisher, J.W., Darrell, T.: Face recognition from long-term observations. In: European Conference on Computer Vision (2002)Google Scholar
  3. 3.
    Li, S.Z., Zhang, Z.: Floatboost learning and statistical face detection. IEEE Transactions of Pattern Analysis and Machine Intelligence 26, 1–12 (2004)CrossRefGoogle Scholar
  4. 4.
    Ariki, Y., Ishikawa, N.: Integration of face and speaker recognition by subspace method. In: Int. Conf. on Pattern Recognition, vol. 3, pp. 456–460 (1996)Google Scholar
  5. 5.
    Yamaguchi, O., Fukui, K., Maeda, K.: Face recognition using temporal image sequence. In: Int. Conf. on Automatic Face and Gesture Recognition, pp. 318–323 (1998)Google Scholar
  6. 6.
    Fukui, K., Yamaguchi, O.: Face recognition using multi-viewpoint patterns for robot vision. In: 10th International Symposium of Robotics Research (2003)Google Scholar
  7. 7.
    Arandjelovic, O., Shakhnarovich, G., Fisher, J., Cipolla, R., Darrell, T.: Face recognition with image sets using manifold density divergence. In: CVPR (2005)Google Scholar
  8. 8.
    Zhou, S., Krueger, V., Chellappa, R.: Probabilistic recognition of human faces from video. Computer Vision Image Understanding 91, 214–245 (2003)CrossRefGoogle Scholar
  9. 9.
    Bichsel, M., Pentland, A.P.: Human face recognition and the face image set’s topology. CVGIP: Image Understanding 59, 254–261 (1994)CrossRefGoogle Scholar
  10. 10.
    Golub, G.H., Van Loan, C.F.: Matrix Computations, 3rd edn. John Hopkins University Press (1996)Google Scholar
  11. 11.
    Stewart, G.W., Sun, J.-G.: Matrix Perturbation Theory. Academic Press Inc., London (1990)MATHGoogle Scholar
  12. 12.
    Bishop, C.M.: Bayesian PCA. Advances in Neural Information Processing Systems 11, 382–388 (1998)Google Scholar
  13. 13.
    Bradski, G., Kaehler, A., Pisarevsky, V.: Learning-based computer vision with Intel’s open source computer vision library. Intel Technology Journal 9, 119–130 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Tat-Jun Chin
    • 1
  • David Suter
    • 1
  1. 1.Institute of Vision Systems EngineeringMonash UniversityVictoriaAustralia

Personalised recommendations