View-Based Recognition of Faces in Man and Machine: Re-visiting Inter-extra-Ortho

  • Christian Wallraven
  • Adrian Schwaninger
  • Sandra Schuhmacher
  • Heinrich H. Bülthoff
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2525)

Abstract

For humans, faces are highly overlearned stimuli, which are encountered in everyday life in all kinds of poses and views. Using psychophysics we investigated the effects of viewpoint on human face recognition. The experimental paradigm is modeled after the inter-extra-ortho experiment using unfamiliar objects by Bülthoff and Edelman [5]. Our results show a strong viewpoint effect for face recognition, which replicates the earlier findings and provides important insights into the biological plausibility of view-based recognition approaches (alignment of a 3D model, linear combination of 2D views and view-interpolation). We then compared human recognition performance to a novel computational view-based approach [29] and discuss improvements of view-based algorithms using local part-based information.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Biederman, I. (1987). Recognition-by-components: A theory of human image understanding. Psychological Review, 94(2), 115–147.CrossRefGoogle Scholar
  2. 2.
    Biederman, I., Gerhardstein, P.C. (1993). Recognizing depth-rotated objects: evidence and conditions for three-dimensional viewpoint invariance. Journal of Experimental Psychology: Human Perception and Performance, 19, 6, 1162–1182.CrossRefGoogle Scholar
  3. 3.
    Biederman, I., Kalocsai, P. (1997). Neurocomputational bases of object and face recognition. Philosophical Transactions of the Royal Society London, B, 352, 1203–1219.CrossRefGoogle Scholar
  4. 4.
    Blanz, V., Vetter, T. (1999). A Morphable Model for the Synthesis of 3D Faces. In Proc. Siggraph99, pp. 187–194.Google Scholar
  5. 5.
    Bülthoff, H.H., Edelman, S. (1992). Psychophysical support for a two-dimensional view interpolation theory of object recognition. PNAS USA, 89, 60–64.Google Scholar
  6. 6.
    Collishaw, S.M., Hole G.J. (2000). Featural and configurational processes in the recognition of faces of different familiarity. Perception, 29, 893–910.CrossRefGoogle Scholar
  7. 7.
    Edelman, S., Bülthoff, H, H. (1992). Orientation dependence in the recognition of familiar and novel views of three-dimensional objects. Vision Research, 32(12), 2385–4000.CrossRefGoogle Scholar
  8. 8.
    Edelman, S. Intrator, N. (2000). A productive, systematic framework for the representation of visual structure. In Proc. NIPS 2000, 10–16.Google Scholar
  9. 9.
    Goren, C., Sarty, M., Wu, P. (1975). Visual following and pattern discrimination of facelike stimuli by newborn infants. Pediatrics, 56, 544–549.Google Scholar
  10. 10.
    Heisele, B., Serre, T., Pontil, M., Vetter, T., and Poggio, T. (2001). Categorization by learning and combining object parts. In Proc. NIPS 2001.Google Scholar
  11. 11.
    Hummel, J.E., Biederman, I. (1992). Dynamic binding in a neural network for shape recognition. Psychological Review, 99(3), 480–517.CrossRefGoogle Scholar
  12. 12.
    Lee, D., Seung S. (1999). Learning the parts of objects by non-negative matrix factorization. Nature, 401:788–791.CrossRefGoogle Scholar
  13. 13.
    Leder, H., Candrian, G., Huber, O., Bruce, V. (2001). Configural features in the context of upright and inverted faces. Perception, 30, 73–83.CrossRefGoogle Scholar
  14. 14.
    Lowe, D.G. (1985). Perceptual organization and visual recognition. Boston: Kluwer Academic Publishing.CrossRefGoogle Scholar
  15. 15.
    Lowe, D.G. (1987). Three-dimensional object recognition from single two-dimensional images. Artificial Intelligence, 31, 355–395.CrossRefGoogle Scholar
  16. 16.
    Marr, D. (1982). Vision. San Francisco: Freeman.Google Scholar
  17. 17.
    Morton, J., Johnson, M.H. (1991). CONSPEC and CONLERN: A two-process theory of infant face recognition. Psychological Review, 98, 164–181.CrossRefGoogle Scholar
  18. 18.
    O'Toole, A. J., Edelman, S., Bülthoff H.H. (1998). Stimulus-specific effects in face recognition over changes in viewpoint. Vision Research, 38, 2351–2363.CrossRefGoogle Scholar
  19. 19.
    Poggio T, Edelman S. (1990) A network that learns to recognize three-dimensional objects. Nature, 18, 343(6255), 263–266.CrossRefGoogle Scholar
  20. 20.
    Pilu, M. (1997). A direct method for stereo correspondence based on singular value decomposition, In Proc. CVPR’97, 261–266.Google Scholar
  21. 21.
    Schwaninger, A., Mast, F. (1999). Why is face recognition so orientation-sensitive? Psychophysical evidence for an integrative model. Perception, 28 (Suppl.), 116.Google Scholar
  22. 22.
    Sergent J. (1985). Influence of task and input factors on hemispheric involvement in face processing. Journal of Experimental Psychology: Human Perception and Performance, 11(6), 846–61.CrossRefGoogle Scholar
  23. 23.
    Schyns, P. G., Rodet, L. (1997) Categorization creates functional features. Journal of Experimental Psychology: Learning, Memory and Cognition, 23, 681–696.CrossRefGoogle Scholar
  24. 24.
    Troje, N. F., Bülthoff, H.H. (1996). Face recognition under varying pose: the role of texture and shape. Vision Research, 36, 1761–1771.CrossRefGoogle Scholar
  25. 25.
    Ullman, S., Basri, R. (1991). Recognition by linear combinations of models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13(10), 992–1006.CrossRefGoogle Scholar
  26. 26.
    Ullman, S., Sali, E. (2000). Object Classification Using a Fragment-Based Representation. In Proc. BMCV’00, 73–87.Google Scholar
  27. 27.
    Valentin, D., Abdi, H., Edelman, B. (1999). From rotation to disfiguration: Testing a dual-strategy model for recognition of faces across view angles. Perception, 28, 817–824.CrossRefGoogle Scholar
  28. 28.
    Wallis, G. M., Bülthoff, H.H. (2001). Effect of temporal association on recognition memory. PNAS USA, 98, 4800–4804.Google Scholar
  29. 29.
    Wallraven, C., Bülthoff, H.H. (2001). Automatic acquisition of exemplar-based representations for recognition from image sequences. CVPR 2001 — Workshop on Models vs. Exemplars.Google Scholar
  30. 30.
    Wallraven, C., Bülthoff, H.H. (2001). View-based recognition under illumination changes using local features. CVPR 2001—Workshop on Identifying Objects Across Variations in Lighting: Psychophysics and Computation.Google Scholar
  31. 31.
    Weber M., Welling M. and Perona P. (2000). Unsupervised Learning of Models for Recognition. In Proc. ECCV2000.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Christian Wallraven
    • 1
  • Adrian Schwaninger
    • 1
    • 2
  • Sandra Schuhmacher
    • 2
  • Heinrich H. Bülthoff
    • 1
  1. 1.Max Planck Institute for Biological CyberneticsTübingenGermany
  2. 2.Department of PsychologyUniversity of ZürichSwitzerland

Personalised recommendations