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Face Recognition with Irregular Region Spin Images

  • Yang Li
  • William A. P. Smith
  • Edwin R. Hancock
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4522)

Abstract

This paper explores how spin images can be constructed using shape-from-shading information and used for the purpose of face recognition. We commence by extracting needle maps from gray-scale images of faces, using a mean needle map to enforce the correct pattern of facial convexity and concavity. Spin images [6] are estimated from the needle maps using local spherical geometry to approximate the facial surface. Our representation is based on spin image histograms for an arrangement of image patches. Comparing to our previous spin image approach, the current one has two basic difference: Euclidean distance is replaced by geodesic distance; Irregular face region is applied to better fit face contour. We demonstrate how this representation can be used to perform face recognition across different subjects and illumination conditions. Experiments show the method to be reliable and accurate, and the recognition precision reaches 93% on CMU PIE sub-database.

Keywords

Face Recognition Image Patch Geodesic Distance Spin Image Active Appearance Model 
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.
    Belhumeur, P., Kriegman, D.: What is the set of images of an object under all possible illumination conditions? International Journal of Computer Vision 28(3), 245–260 (1998)CrossRefGoogle Scholar
  2. 2.
    Blanz: Automatic face identification system using flexible appearance models. IVC 13(5), 393–401 (1995), citeseer.ist.psu.edu/article/lanitis95automatic.html Google Scholar
  3. 3.
    Blanz, V., Vetter, T.: Face recognition based on fitting a 3d morphable model. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(9), 1063–1074 (2003)CrossRefGoogle Scholar
  4. 4.
    Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active appearance models. In: Burkhardt, H., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1407, pp. 484–498. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  5. 5.
    Dijkstra, E.W.: A note on two problems in connexion with graphs. In: Numerische Mathematik, vol. 1, pp. 269–271. Mathematisch Centrum, Amsterdam, The Netherlands (1959)Google Scholar
  6. 6.
    Johnson, A.E., Hebert, M.: Using spin images for efficient object recognition in cluttered 3d scenes. IEEE Transactions on Pattern Analysis and Machine Intelligence 21(5), 433–449 (1999), citeseer.ist.psu.edu/johnson99using.html CrossRefGoogle Scholar
  7. 7.
    Li, Y., Hancock, E.: Face recognition using shading-based curvature attributes. In: International Conference on Pattern Recognition, ICPR, Cambridge, UK (August 2004)Google Scholar
  8. 8.
    Frankot, R.T., Chellappa, Z.: A method for enforcing integrability in shape from shading algorithms. IEEE Transactions in Pattern Recognition and Machine Intelligence 10, 439–451 (1988)zbMATHCrossRefGoogle Scholar
  9. 9.
    Sim, T., Kanade, T.: Combining models and exemplars for face recognition: An illuminating example. In: Proceedings of the CVPR 2001 Workshop on Models versus Exemplars in Computer Vision (December 2001)Google Scholar
  10. 10.
    Smith, W.A.P., Hancock, E.R.: Recovering facial shape and albedo using a statistical model of surface normal direction. In: Proc. ICCV, pp. 588–595 (2005)Google Scholar
  11. 11.
    Turk, M., Pentland, A.: Face recognition using eigenfaces. In: IEEE Conference on Computer Vision and Pattern Recognition, IEEE Computer Society Press, Los Alamitos (1991)Google Scholar
  12. 12.
    Worthington, P.L., Hancock, E.R.: New constraints on data-closeness and needle map consistency for shape-from-shaping. IEEE Transactions on Pattern Analysis and Machine Intelligence 21(12), 1250–1267 (1999)CrossRefGoogle Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Yang Li
    • 1
  • William A. P. Smith
    • 1
  • Edwin R. Hancock
    • 1
  1. 1.Department of Computer Science, University of York, York, YO10 5DDUK

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