Skip to main content
SpringerLink
Log in

Role of Featural and Configural Information in Familiar and Unfamiliar Face Recognition

  • Conference paper
  • First Online:
Book cover Biologically Motivated Computer Vision (BMCV 2002)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2525))

Included in the following conference series:

Abstract

Using psychophysics we investigated to what extent human face recognition relies on local information in parts (featural information) and on their spatial relations (configural information). This is particularly relevant for biologically motivated computer vision since recent approaches have started considering such featural information. In Experiment 1 we showed that previously learnt faces could be recognized by human subjects when they were scrambled into constituent parts. This result clearly indicates a role of featural information. Then we determined the blur level that made the scrambled part versions impossible to recognize. This blur level was applied to whole faces in order to create configural versions that by definition do not contain featural information. We showed that configural versions of previously learnt faces could be recognized reliably. In Experiment 2 we replicated these results for familiar face recognition. Both Experiments provide evidence in favor of the view that recognition of familiar and unfamiliar faces relies on featural and configural information. Furthermore, the balance between the two does not differ for familiar and unfamiliar faces. We propose an integrative model of familiar and unfamiliar face recognition and discuss implications for biologically motivated computer vision algorithms for face recognition.

AS was supported by a grant from the European Commission (IST Programme).

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Heisele, B., Serre, T., Pontil, M., Vetter, T., and Poggio, T. (2001). Categorization by learning and combining object parts. NIPS proceedings.

    Google Scholar 

  2. Lee, D.D., & Seung, S. (1999). Learning the parts of objects by non-negative matrix factorization. Nature, 401, 788–791.

    Article  Google Scholar 

  3. Ullman, S., & Sali, E. (2000). Object Classification Using a Fragment-Based Representation. BMCV 2000. Lecture Notes in Computer Science, 1811, pp. 73–87. Berlin: Springer.

    Google Scholar 

  4. Tanaka J. W. & Farah, M. J. (1991). Second-order relational properties and the inversion-effect: Testing a theory of face perception. Perception & Psychophysics, 50, 367–372.

    Article  Google Scholar 

  5. Tanaka, J. W. & Farah, M. J. (1993). Parts and wholes in face recognition. Quarterly Journal of Experimental Psychology, 79, 471–491.

    Google Scholar 

  6. Farah, M. J., Tanaka, J. W., & Drain, H. M. (1995). What causes the face inversion effect? Journal of Experimental Psychology: Human Perception and Performance, 21, (3), 628–634.

    Article  Google Scholar 

  7. Lades, M., Vorbrüggen, J.C., Buhmann, J., Lange, J., Malsburg, v.d., C., Würtz, R.P., & Konen, W. (1993). Distortion invariant object recognition in the dynamic link architecture. IEEE Transactions on Computers, 42, 300–311.

    Article  Google Scholar 

  8. Wiskott, L., Fellous, J.M., Krüger, N., & von der Malsburg, C. (1997). Face Recognition by Elastic Bunch Graph Matching. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(7), 775–779.

    Article  Google Scholar 

  9. Biedermann, I., & Kalocsai, P. (1997). Neurocomputational bases of object and face recognition. Philosophical Transactions of the Royal Society of London, B, 352, 1203–1219.

    Article  Google Scholar 

  10. Rhodes, G., Brake, S., & Atkinson, A.P. (1993). What’s lost in inverted faces? Cognition, 47, 25–57.

    Article  Google Scholar 

  11. Rakover, S. S. (2002). Featural vs. configurational information in faces: A conceptual and empirical analysis. British Journal of Psychology, 93, 1–30.

    Article  Google Scholar 

  12. Collishaw, S.M., Hole G.J. (2000). Featural and configurational processes in the recognition of faces of different familiarity. Perception, 29, 893–910.

    Article  Google Scholar 

  13. Davidoff, J., & Donnelly, N. (1990). Object superiority: a comparison of complete and part probes, Acta Psychologica, 73 1990 225–243.

    Article  Google Scholar 

  14. 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.

    Article  Google Scholar 

  15. Green, D.M., & Swets, J.A. (1966). Signal detection theory and psychophysics. New York: Wiley.

    Google Scholar 

  16. McMillan, N.A., & Creelman, C.D. (1992). Detection theory: A user’s guide. New York: Cambridge University Press.

    Google Scholar 

  17. Benton AL, 1980. The neuropsychology of facial recognition. American Psychologist, 35, 176–186.

    Google Scholar 

  18. Malone D.R., Morris H.H., Kay M.C., Levin, H.S., 1982. Prosopagnosia: a double dissociation between the recognition of familiar and unfamiliar faces. Journal of Neurology, Neurosurgery, and Psychiatry, 45, 820–822.

    Google Scholar 

  19. Ellis, H.D., Shepherd, J.W., Davies G.M., 1979. Identification of familiar and unfamiliar faces from internal and external features: some implications for theories of face recognition. Perception, 8, 431–439

    Google Scholar 

  20. Yarmey, A.D., 1971. Recognition memory for familiar “public” faces: Effects of orientation and delay. Psychonomic Science, 24, 286–288.

    Google Scholar 

  21. Hasselmo, M.E., Rolls, E.T., & Baylis, C.G. (1989). The role of expression and identity in the face-selective responses of neurons in the temporal visual cortex of the monkey. Experimental Brain Research, 32, 203–218.

    Google Scholar 

  22. Perret, D.I., Hietanen, J.K., Oram, M.W., & Benson, P.J. (1992). Organization and functions of cells in the macaque temporal cortex. Philosophical Transactions of the Royal Society of London, B, 335, 23–50.

    Article  Google Scholar 

  23. Perret, D.I., Rolls, E.T., & Caan, W. (1982). Visual neurones responsive to faces in the monkey temporal cortex. Experimental Brain Research, 47, 329–342.

    Google Scholar 

  24. Perret, D.I., Mistlin, A.J., & Chitty, A.J. (1987). Visual neurones responsive to faces. Trends in Neuroscience, 10, 358–364.

    Article  Google Scholar 

  25. Perret, D.I., & Oram, M.W. (1993). Image Vis. Comput., 11, 317–333.

    Article  Google Scholar 

  26. Wachsmuth, E., Oram, M.W., & Perret, D.I. (1994). Recognition of objects and their component parts: responses of single units in the temporal cortex of the macaque. Cerebral Cortex, 4, 509–522.

    Article  Google Scholar 

  27. Yamane, S., Kaji, S., & Kawano, K. (1988). What facial features activate face neurons in the inferotemporal cortex of the monkey? Experimental Brain Research, 73, 209–214.

    Article  Google Scholar 

  28. Schwaninger, Lobmaier, & Collishaw (2002). Role and interaction of featural and configural processing in face recognition. Vision Sciences Society, 2nd annual meeting, Sarasota, Florida, May 10–15, 2002.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Max Planck Institute for Biological Cybernetics, Tübingen, Germany

    Adrian Schwaninger

  2. Department of Psychology, University of Zürich, Switzerland

    Adrian Schwaninger & Janek S. Lobmaier

  3. School of Cognitive and Computing Sciences, University of Sussex, UK

    Stephan M. Collishaw

Authors
  1. Adrian Schwaninger
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Janek S. Lobmaier
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Stephan M. Collishaw
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Max Planck Institute for Biological Cybernetics, Spemannstraße 38, 72076, Tübingen, Germany

    Heinrich H. Bülthoff  & Christian Wallraven  & 

  2. Department of Computer Science and Engineering, Korea University, Anam-dong, Seongbuk-ku, 136-701, Seoul, Korea

    Seong-Whan Lee

  3. Department of Brain and Cognitive Sciences, Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 45 Carleton Street, 02142, Cambridge, MA, USA

    Tomaso A. Poggio

Rights and permissions

Reprints and permissions

Copyright information

© 2002 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Schwaninger, A., Lobmaier, J.S., Collishaw, S.M. (2002). Role of Featural and Configural Information in Familiar and Unfamiliar Face Recognition. In: Bülthoff, H.H., Wallraven, C., Lee, SW., Poggio, T.A. (eds) Biologically Motivated Computer Vision. BMCV 2002. Lecture Notes in Computer Science, vol 2525. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36181-2_64

Download citation

  • DOI: https://doi.org/10.1007/3-540-36181-2_64

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-00174-4

  • Online ISBN: 978-3-540-36181-7

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation