Randomized Face Recognition on Partially Occluded Images

  • Ariel Morelli Andres
  • Sebastian Padovani
  • Mariano Tepper
  • Marta Mejail
  • Julio Jacobo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7441)

Abstract

In this work we propose a new method for face recognition that successfully handles occluded faces. We propose an innovative improvement that allows to detect and discard occluded zones of the face, thus making recognition more robust in the presence of occlusion. We provide experimental results that show that the proposed method performs well in practice.

References

  1. 1.
    Turk, M., Pentland, A.: Eigenfaces for recognition. Journal of Cognitive Neuroscience 3(1), 71–86 (1991)CrossRefGoogle Scholar
  2. 2.
    Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active appearance models. IEEE Transactions on Pattern Analysis and Machine Intelligence 23, 681–685 (2001)CrossRefGoogle Scholar
  3. 3.
    Stegmann, M.B., Ersbøll, B.K., Larsen, R.: FAME - A Flexible Appearance Modelling Environment. IEEE Transactions on Medical Imaging 22(10), 1319–1331 (2003)CrossRefGoogle Scholar
  4. 4.
    Kahraman, F., Kurt, B., Gokmen, M.: Robust face alignment for illumination and pose invariant face recognition. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007)Google Scholar
  5. 5.
    Belhumeur, P.N., Hespanha, J., Kriegman, D.J.: Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(7), 711–720 (1997)CrossRefGoogle Scholar
  6. 6.
    Wright, J., Yang, A.Y., Ganesh, A., Sastry, S.S., Ma, Y.: Robust face recognition via sparse representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 31(2), 210–227 (2009)CrossRefGoogle Scholar
  7. 7.
    Candès, E.J., Romberg, J.K., Tao, T.: Stable signal recovery from incomplete and inaccurate measurements. Communications on Pure and Applied Mathematics 59(8), 1207–1223 (2006)MathSciNetMATHCrossRefGoogle Scholar
  8. 8.
    Candès, E.J., Tao, T.: Near-optimal signal recovery from random projections: Universal encoding strategies? IEEE Transactions on Information Theory 52(12), 5406–5425 (2006)CrossRefGoogle Scholar
  9. 9.
    Candès, E.J., Wakin, M.B.: An introduction to compressive sampling. IEEE Signal Processing Magazine 25(2), 21–30 (2008)CrossRefGoogle Scholar
  10. 10.
    Lin, D., Tang, X.: Quality-driven face occlusion detection and recovery. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007)Google Scholar
  11. 11.
    Zhou, Z., Wagner, A., Mobahi, H., Wright, J., Ma, Y.: Face recognition with contiguous occlusion using markov random fields. In: International Conference on Computer Vision, ICCV (2009)Google Scholar
  12. 12.
    Basri, R., Jacobs, D.W.: Lambertian reflectance and linear subspaces. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(2), 218–233 (2003)CrossRefGoogle Scholar
  13. 13.
    Lee, K.C., Ho, J., Kriegman, D.J.: Acquiring linear subspaces for face recognition under variable lighting. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(5), 684–698 (2005)CrossRefGoogle Scholar
  14. 14.
    Donoho, D.L.: For most large underdetermined systems of linear equations the minimal, L1-norm solution is also the sparsest solution. Communications on Pure and Applied Mathematics 59, 797–829 (2004)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Martinez, A.M., Benavente, R.: The AR face database. Technical Report 24, CVC (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Ariel Morelli Andres
    • 1
  • Sebastian Padovani
    • 1
  • Mariano Tepper
    • 1
  • Marta Mejail
    • 1
  • Julio Jacobo
    • 1
  1. 1.Departamento de Computación, Facultad de Ciencias Exactas y NaturalesUniversidad de Buenos AiresArgentina

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