Advertisement

Second-Order (Non-Fourier) Attention-Based Face Detection

  • Albert L. Rothenstein
  • Andrei Zaharescu
  • John K. Tsotsos
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4132)

Abstract

We present an attention-based face detection and localization system. The system is biologically motivated, combining face detection based on second-order circular patterns with the localization capabilities of the Selective Tuning (ST) model of visual attention [1]. One of the characteristics of this system is that the face detectors are relatively insensitive to the scale and location of the face, and thus additional processing needs to be performed to localize the face for recognition. We extend ST’s ability to recover spatial information to this object recognition system, and show how this can be used to precisely localize faces in images. The system presented in this paper exhibits temporal characteristics that are qualitatively similar to those of the primate visual system in that detection and categorization is performed early in the processing cycle, while detailed information needed for recognition is only available after additional processing, consistent with experimental data and with certain theories of visual object recognition [2].

Keywords

Visual Attention Face Detection Illusory Contour Pyramidal Structure Visual Object Recognition 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Tsotsos, J.K., Culhane, S.M., Wai, W.Y.K., Lai, Y.H., Davis, N., Nuflo, F.: Modeling visual-attention via selective tuning. Artif. Intell. 78(1-2), 507–545 (1995)CrossRefGoogle Scholar
  2. 2.
    Grill-Spector, K., Kanwisher, N.: Visual recognition: as soon as you see it, you know what it is. Psychological Science 16(2), 152–160 (2005)CrossRefGoogle Scholar
  3. 3.
    Lowe, D.G.: Perceptual organization and Visual Recognition. Kluwer, Dordrecht (1985)Google Scholar
  4. 4.
    Kanizsa, G.: Organization in Vision: Essays on Gestalt Perception. Praeger, Westport (1979)Google Scholar
  5. 5.
    Koffka, K.: Principles of Gestalt Psychology. Kegan Paul, London (1936)Google Scholar
  6. 6.
    Zucker, S.W.: Computational and psychophysical experiments in grouping: Early orientation selection. In: Beck, J., Hope, B., Rosenfeld, A. (eds.) Human and Machine Vision, pp. 545–567. Academic Press, London (1983)Google Scholar
  7. 7.
    Grigorescu, C., Petkov, N., Westenberg, M.A.: Contour and boundary detection improved by surround suppression of texture edges. Image and Vision Computing 22(8), 609–622 (2004)CrossRefGoogle Scholar
  8. 8.
    Hjelmåsa, E., Low, B.K.: Face detection: A survey. Computer Vision and Image Understanding 83(3), 236–274 (2001)CrossRefGoogle Scholar
  9. 9.
    Yang, M.H., Kriegman, D.J., Ahuja, N.: Detecting faces in images: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(1), 34–58 (2002)CrossRefGoogle Scholar
  10. 10.
    Turk, M., Pentland, A.: Eigenfaces for recognition. Cognitive Neuroscience 13(1), 71–96 (1991)Google Scholar
  11. 11.
    Moghaddam, B., Pentland, A.: Probabilistic visual learning for object representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(7), 696–710 (1997)CrossRefGoogle Scholar
  12. 12.
    Sung, K.K., Poggio, T.: Example-based learning for view-based human face detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(1), 39–51 (1998)CrossRefGoogle Scholar
  13. 13.
    Viola, P., Jones, M.: Robust real-time object detection. In: ICCV 2001 Workshop on Statistical and Computation Theories of Vision (2001)Google Scholar
  14. 14.
    Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: IEEE Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 511–518 (2001)Google Scholar
  15. 15.
    Jones, M., Viola, P.: Fast multi-view face detection. In: IEEE Conference on Computer Vision and Pattern Recognition (2003)Google Scholar
  16. 16.
    Zhang, L., Li, S.Z., Qu, Z.Y., Huang, X.: Boosting local feature based classifiers for face recognition. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Washington, D.C., USA, vol. 5, p. 87 (2004)Google Scholar
  17. 17.
    Mikolajczyk, K., Schmid, C., Zisserman, A.: Human detection based on a probabilistic assembly of robust part detectors. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3021, pp. 69–82. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  18. 18.
    Tsotsos, J.K.: A complexity level analysis of immediate vision. International Journal of Computer Vision 1(4), 303–320 (1987)CrossRefGoogle Scholar
  19. 19.
    Tsotsos, J.K., Liu, Y., Martinez-Trujillo, J.C., Pomplun, M., Simine, E., Zhou, K.: Attending to visual motion. Comput. Vis. Image Und. 100(1-2), 3–40 (2005)CrossRefGoogle Scholar
  20. 20.
    Rothenstein, A.L., Tsotsos, J.K.: Attention links sensing to recognition. Image and Vision Computing (2006) (in press) doi:10.1016/j.imavis.2005.08.011Google Scholar
  21. 21.
    Hubel, D., Wiesel, T.N.: Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. Journal of Physiology (160), 106–154 (1962)Google Scholar
  22. 22.
    Dreher, B.: Hypercomplex cells in the cat’s striate cortex. Invest Ophthalmol. 5(11), 355–356 (1972)Google Scholar
  23. 23.
    Dobbins, A., Zucker, S.W., Cynader, M.S.: Endstopped neurons in the visual cortex as a substrate for calculating curvature. Nature 329, 438–441 (1987)CrossRefGoogle Scholar
  24. 24.
    Koenderink, J.J., Richards, W.A.: Two-dimensional curvature operators. J. Opt. Soc. Am. A 52, 1136–1141 (1988)CrossRefMathSciNetGoogle Scholar
  25. 25.
    Fleet, D., Black, M., Jepson, A.: Motion feature detection using steerable flow fields. In: Proceedings of the IEEE Computer Vision and Pattern Recognition Conference (CVPR), pp. 274–281 (1998)Google Scholar
  26. 26.
    von der Heydt, R., Peterhans, E., Baumgartner, G.: Illusory contours and cortical neuron responses. Science 224, 1260–1262 (1984)CrossRefGoogle Scholar
  27. 27.
    Gallant, J., Braun, J., Van Essen, D.C.: Selectivity for polar, hyperbolic, and cartesian gratings in macaque visual cortex. Science 259, 100–103 (1993)CrossRefGoogle Scholar
  28. 28.
    Gallant, J.L., Connor, C.E., Rakshit, S., Lewis, J., Van Essen, D.C.: Neural responses to polar, hyperbolic, and cartesian gratings in area V4 of the macaque monkey. Journal of Neurophysiology 76, 2718–2737 (1996)Google Scholar
  29. 29.
    Wilson, H.R.: Non-Fourier cortical processes in texture, form, and motion perception. In: Ulinski, P.S., Jones, E.G. (eds.) Cerebral Cortex, vol. 13. Kluwer Academic/ Plenum Publishers, New York (1999)Google Scholar
  30. 30.
    Rothenstein, A.L., Zaharescu, A., Tsotsos, J.K.: A general purpose neural network simulator for visual attention modeling. In: Paletta, L., Tsotsos, J.K., Rome, E., Humphreys, G.W. (eds.) WAPCV 2004. LNCS, vol. 3368, pp. 159–167. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  31. 31.
    Bellhumer, P.N., Hespanha, J., Kriegman, D.: Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence 17(7), 711–720 (1997)CrossRefGoogle Scholar
  32. 32.
    Georghiades, A., Belhumeur, P., Kriegman, D.: From few to many: Illumination cone models for face recognition under variable lighting and pose. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(6), 643–660 (2001)CrossRefGoogle Scholar
  33. 33.
    Lamme, V.A.F., Roelfsema, P.R.: The distinct modes of vision offered by feedforward and recurrent processing. Trends in Neurosciences 23(11), 571–579 (2000)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Albert L. Rothenstein
    • 1
  • Andrei Zaharescu
    • 2
  • John K. Tsotsos
    • 1
  1. 1.Dept. of Computer Science & Engineering and Centre for Vision ResearchYork UniversityTorontoCanada
  2. 2.INRIA Rhone-AlpesMontbonnotFrance

Personalised recommendations