Skip to main content
SpringerLink
Log in

Gender Classification of Human Faces

  • Conference paper
  • First Online:
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

This paper addresses the issue of combining pre-processing methods—dimensionality reduction using Principal Component Analysis (PCA) and Locally Linear Embedding (LLE)—with Support Vector Machine (SVM) classification for a behaviorally important task in humans: gender classification. A processed version of the MPI head database is used as stimulus set. First, summary statistics of the head database are studied. Subsequently the optimal parameters for LLE and the SVM are sought heuristically. These values are then used to compare the original face database with its processed counterpart and to assess the behavior of a SVM with respect to changes in illumination and perspective of the face images. Overall, PCA was superior in classification performance and allowed linear separability.

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. A.J. O'Toole, K.A. Deffenbacher, D. Valentin, K. McKee, D. Hu. and H. Abdi. The Perception of Face Gender: the Role of Stimulus Structure in Recognition and Classification. Memory & Cognition, 26(1), 1998.

    Google Scholar 

  2. B. Moghaddam and M.-H. Yang. Gender Classification with Support Vector Machines. Proceedings of the International Conference on Automatic Face and Gesture Recognition (FG), 2000.

    Google Scholar 

  3. V. Blanz and T. Vetter. A Morphable Model for the Synthesis of 3D Faces. Proc. Siggraph99, pp. 187–194. Los Angeles: ACM Press, 1999.

    Google Scholar 

  4. R. O. Duda and P.E. Hart and D.G. Stork. Pattern Classification. John Wiley & Sons, 2001.

    Google Scholar 

  5. L. Sirovich, and M. Kirby. Low-Dimensional Procedure for the Characterization of Human Faces. Journal of the Optical Society of America A, 4(3), 519–24, 1987.

    Article  Google Scholar 

  6. M. Turk and A. Pentland. Eigenfaces for Recognition. Journal of Cognitive Neuroscience, 3(1), 71–86, 1991.

    Article  Google Scholar 

  7. A.J. O'Toole, H. Abdi, K.A. Deffenbacher and D. Valentin. Low-Dimensional Representation of Faces in Higher Dimensions of the Face Space. Journal of the Optical Society of America A, 10(3), 405–11, 1993.

    Article  Google Scholar 

  8. S. T. Roweis and L.K. Saul. Nonlinear Dimensionality Reduction by Locally Linear Embedding. Science, 290, 2000.

    Google Scholar 

  9. L.K. Saul and S. T. Roweis. An Introduction to Locally Linear Embedding. Report at AT&T Labs-Research, 2000.

    Google Scholar 

  10. B. Schölkopf, J. C. Platt, J. Shawe-Taylor, A. Smola and R.C. Williamson. Estimating the Support of a High-Dimensional Distribution. Neural Computation, 13(7), 2001.

    Google Scholar 

  11. V. N. Vapnik. The Nature of Statistical Learning Theory. Springer, 1995.

    Google Scholar 

  12. A. B. A. Graf and S. Borer. Normalization in Support Vector Machines. Proceedings of the DAGM, LNCS 2191, 2001.

    Google Scholar 

  13. V. Bruce, T. Valentine and A.D. Baddeley. The Basis of the 3/4 View Advantage in Face Recognition. Applied Cognitive Psychology, 1:109–120, 1987.

    Article  Google Scholar 

  14. H. Jaeger, The “Echo State” Approach to Analysing and Training Recurrent Neural Networks. GMD Report 148, German National Research Center for Information Technology, 2001.

    Google Scholar 

  15. W. Maass, T. Natschläger, and H. Markram. Real-Time Computing without Stable States: A New Framework for Neural Computation Based on Perturbations. Neural Computation, 2002 (in press).

    Google Scholar 

  16. M. Baenninger. The Development of Face Recognition: Featural or Configurational Processing? Journal of Experimental Child Psychology, 57(3), 377–96, 1994.

    Article  Google Scholar 

  17. D. D. Lee and H. S. Seung. Learning the Parts of Objects by Non-Negative Matrix Factorization. Nature, 401:788–791, 1999.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

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

    Arnulf B.A. Graf & Felix A. Wichmann

Authors
  1. Arnulf B.A. Graf
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Felix A. Wichmann
    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

Graf, A.B., Wichmann, F.A. (2002). Gender Classification of Human Faces. 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_49

Download citation

  • DOI: https://doi.org/10.1007/3-540-36181-2_49

  • 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