An Analysis of Automatic Gender Classification

  • Modesto Castrillón-Santana
  • Quoc C. Vuong
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4756)

Abstract

Different researches suggest that inner facial features are not the only discriminative features for tasks such as person identification or gender classification. Indeed, they have shown an influence of features which are part of the local face context, such as hair, on these tasks. However, object-centered approaches which ignore local context dominate the research in computational vision based facial analysis. In this paper, we performed an analysis to study which areas and which resolutions are diagnostic for the gender classification problem. We first demonstrate the importance of contextual features in human observers for gender classification using a psychophysical ”bubbles” technique. The success rate achieved without internal facial information convinced us to analyze the performance of an appearance-based representation which takes into account facial areas and resolutions that integrate inner features and local context.

Keywords

Gender classification local context PCA SVM 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Abdi, H., Valentin, D., Edelman, B.G., O’Toole, A.J.: More about the difference between men and women: evidence from linear neural network and the principal component approach. Perception 24 (1995)Google Scholar
  2. 2.
    Brunelli, R., Poggio, T.: Hyperbf networks for gender classification. In: Proceedings of the DARPA Image Understanding Workshop, pp. 311–314 (1992)Google Scholar
  3. 3.
    Moghaddam, B., Yang, M.-H.: Learning gender with support faces. IEEE Trans. on Pattern Analysis and Machine Intelligence 24(5), 707–711 (2002)CrossRefGoogle Scholar
  4. 4.
    Wu, J., Smith, W.A.P., Hancock, E.R.: Learning mixture models for gender classification based on facial surface normals. In: 3rd Iberian Conference on Pattern Recognition and Image Analysis, Girona, Spain, June 2007, pp. 39–46 (2007)Google Scholar
  5. 5.
    Lapedriza, A., Masip, D., Vitria, J.: Are external face features useful for automatic face classification? In: CVPR 2005, vol. 3, pp. 151–157 (2005)Google Scholar
  6. 6.
    Yacoob, Y., Davis, L.S.: Detection and analysis of hair. IEEE Trans. on Pattern Analysis and Machine Intelligence 28(7), 1164–1169 (2006)CrossRefGoogle Scholar
  7. 7.
    Bruce, V., Young, A.: The eye of the beholder. Oxford University Press, Oxford (1998)Google Scholar
  8. 8.
    Jarudi, I., Sinha, P.: Relative roles of internal and external features in face recognition. Technical Report memo 225, CBCL (2005)Google Scholar
  9. 9.
    Sinha, P., Poggio, T.: I think I know that face... Nature 384(6608), 384–404 (1996)Google Scholar
  10. 10.
    Sinha, P., Torralba, A.: Detecting faces in impoverished images. AI memo 2001-028, CBCL memo 208, Massachussets Institute of Technology (2001)Google Scholar
  11. 11.
    Torralba, A.: Contextual priming for object detection. International Journal of Computer Vision 53(2), 169–191 (2003)CrossRefGoogle Scholar
  12. 12.
    Gosselin, F., Schyns, P.G.: Bubbles: a technique to reveal the use of information in recognition tasks. Vision Research, 2261–2271 (2001)Google Scholar
  13. 13.
    Vapnik, V.: The nature of statistical learning theory. Springer, New York (1995)MATHGoogle Scholar
  14. 14.
    Castrillón, M., Déniz, O., Hernández, M., Guerra, C.: ENCARA2: Real-time detection of multiple faces at different resolutions in video streams. Journal of Visual Communication and Image Representation, 130–140 (2007)Google Scholar
  15. 15.
    Wersing, H., Kirstein, S., Goetting, M., Brandl, H., Dunn1, M., Mikhailova, I., Goerick, C., Steil, J., Ritter, H., Kierner, E.: Online learning of objects and faces in an integrated biologically motivated architecture. In: ICVS (2007)Google Scholar
  16. 16.
    Xie, X., Lam, K.-M.: An efficient illumination normalization method for face recognition. Pattern Recognition Letters 27(6), 609–617 (2006)CrossRefGoogle Scholar
  17. 17.
    Artac, M., Jogan, M., Leonardis, A.: Incremental PCA for on-line visual learning and recognition. In: Proceedings 16th International Conference on Pattern Recognition, pp. 781–784 (2002)Google Scholar
  18. 18.
    Hall, P., Marshall, D., Martin, R.: Incremental eigenanalysis for classification. In: British Machine Vision Conference, vol. 1, pp. 286–295 (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Modesto Castrillón-Santana
    • 1
  • Quoc C. Vuong
    • 2
  1. 1.IUSIANI, Edificio Central del Parque Científico-Tecnológico, Universidad de Las Palmas de Gran Canaria, 35017 Las PalmasSpain
  2. 2.Division of Psychology, Henry Wellcome Building for Neuroecology, Newcastle University, Framlington Place, Newcastle upon Tyne, NE2 4HHUK

Personalised recommendations