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Unfolding a Face: From Singular to Manifold

  • Ognjen Arandjelović
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5996)

Abstract

Face recognition from a single image remains an important task in many practical applications and a significant research challenge. Some of the challenges are inherent to the problem, for example due to changing lighting conditions. Others, no less significant, are of a practical nature – face recognition algorithms cannot be assumed to operate on perfect data, but rather often on data that has already been subject to pre-processing errors (e.g. localization and registration errors). This paper introduces a novel method for face recognition that is both trained and queried using only a single image per subject. The key concept, motivated by abundant prior work on face appearance manifolds, is that of face part manifolds – it is shown that the appearance seen through a sliding window overlaid over an image of a face, traces a trajectory over a 2D manifold embedded in the image space. We present a theoretical argument for the use of this representation and demonstrate how it can be effectively exploited in the single image based recognition. It is shown that while inheriting the advantages of local feature methods, it also implicitly captures the geometric relationship between discriminative facial features and is naturally robust to face localization errors. Our theoretical arguments are verified in an experimental evaluation on the Yale Face Database.

Keywords

Face Recognition Face Image Image Space Face Part Face Recognition Algorithm 
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.
    Lui, Y.M., Beveridge, J.R.: Grassmann registration manifolds for face recognition, vol. 2, pp. 44–57 (2008)Google Scholar
  2. 2.
    Mita, T., Kaneko, T., Stenger, B., Hori, O.: Discriminative feature co-occurrence selection for object detection. PAMI 30(7), 1257–1269 (2008)Google Scholar
  3. 3.
    Matas, J., Bilek, P., Hamouz, M., Kittler, J.: Discriminative regions for human face detection (2002)Google Scholar
  4. 4.
    Sivic, J., Everingham, M., Zisserman, A.: Person spotting: video shot retrieval for face sets, pp. 226–236 (2005)Google Scholar
  5. 5.
    Arca, S., Campadelli, P., Lanzarotti, R.: A face recognition system based on local feature analysis. In: Kittler, J., Nixon, M.S. (eds.) AVBPA 2003. LNCS, vol. 2688, pp. 182–189. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  6. 6.
    Bolme, D.S.: Elastic bunch graph matching. Master’s thesis, Colorado State University (2003)Google Scholar
  7. 7.
    Heo, J., Abidi, B., Paik, J., Abidi, M.A.: Face recognition: Evaluation report for FaceIt®. In: Proc. International Conference on Quality Control by Artificial Vision, vol. 5132, pp. 551–558 (2003)Google Scholar
  8. 8.
    Stergiou, A., Pnevmatikakis, A., Polymenakos, L.: EBGM vs. subspace projection for face recognition. In: Proc. International Conference on Computer Vision Theory and Applications (2006)Google Scholar
  9. 9.
    Lee, K., Kriegman, D.: Online learning of probabilistic appearance manifolds for video-based recognition and tracking, vol. 1, pp. 852–859 (2005)Google Scholar
  10. 10.
    Arandjelović, O., Cipolla, R.: A pose-wise linear illumination manifold model for face recognition using video, vol. 113 (2008)Google Scholar
  11. 11.
    Sim, T., Zhang, S.: Exploring face space. In: Proc. IEEE Workshop on Face Processing in Video, p. 84 (2004)Google Scholar
  12. 12.
    Arandjelović, O., Cipolla, R.: Face recognition from video using the generic shape-illumination manifold, vol. 4, pp. 27–40 (2006)Google Scholar
  13. 13.
    Fitzgibbon, A., Zisserman, A.: On affine invariant clustering and automatic cast listing in movies, pp. 304–320 (2002)Google Scholar
  14. 14.
    Gao, Y., Wang, Y., Feng, X., Zhou, X.: Face recognition using most discriminative local and global features, vol. 1, pp. 351–354 (2006)Google Scholar
  15. 15.
    Georghiades, A.S., Belhumeur, P.N., Kriegman, D.J.: From few to many: Illumination cone models for face recognition under variable lighting and pose 23(6), 643–660 (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Ognjen Arandjelović
    • 1
  1. 1.Trinity CollegeUniversity of CambridgeCambridgeUnited Kingdom

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