Semi-supervised Feature Selection for Gender Classification

  • Jing Wu
  • William A. P. Smith
  • Edwin R. Hancock
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5995)


We apply a semi-supervised learning method to perform gender determination. The aim is to select the most discriminating feature components from the eigen-feature representation of faces. By making use of the information provided by both labeled and unlabeled data, we successfully reduce the size of the labeled data set required for gender feature selection, and improve the classification accuracy. Instead of using 2D brightness images, we use 2.5D facial needle-maps which reveal more directly facial shape information. Principal geodesic analysis (PGA), which is a generalization of principal component analysis (PCA) from data residing in a Euclidean space to data residing on a manifold, is used to obtain the eigen-feature representation of the facial needle-maps. In our experiments, we achieve 90.50% classification accuracy when 50% of the data are labeled. This performance demonstrates the effectiveness of this method for gender classification using a small labeled set, and the feasibility of gender classification using the facial shape information.


Feature Selection Harmonic Function Linear Discriminant Analysis Label Data Unlabeled Data 
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.


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  1. 1.
    Golomb, B., Lawrence, D., Sejnowski, T.: SexNet: A Neural Network Identifies Sex from Human Faces. In: Advances in Neural Information Processing Systems, pp. 572–577 (1991)Google Scholar
  2. 2.
    Cottrell, G.W., Metcalfe, J.: Face, Emotion, and Gender Recognition Using Holons. In: Advances in Neural Information Processing Systems, vol. 3, pp. 564–571 (1991)Google Scholar
  3. 3.
    Sun, Z., Bebis, G., Yuan, X., Louis, S.J.: Genetic Feature Subset Selection for Gender Classification: A Comparison Study. In: WACV 2002, pp. 165–170 (2002)Google Scholar
  4. 4.
    Buchala, S., Davey, N., Gale, T.M., Frank, R.J.: Principal Component Analysis of Gender, Ethnicity, Age, and Identity of Face Images. In: Proc. IEEE ICMI 2005 (2005)Google Scholar
  5. 5.
    Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active Appearance Models. In: Burkhardt, H., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1407, pp. 484–498. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  6. 6.
    Jain, A., Huang, J.: Integrating Independent Components and Linear Discriminant Analysis for Gender Classification. In: FGR 2004, pp. 159–163 (2004)Google Scholar
  7. 7.
    Gutta, S., Weschler, H., Phillips, P.J.: Gender and Ethnic Classification of Human Faces using Hybrid Classifiers. In: Proc. of IEEE International Conf. on Automatic Face and Gesture Recognition, pp. 194–199 (1998)Google Scholar
  8. 8.
    Moghaddam, B., Yang, M.H.: Learning gender with support faces. IEEE Transaction Pattern Analysis and Machine Intelligence 24(5), 707–711 (2002)CrossRefGoogle Scholar
  9. 9.
    Baluja, S., Rowley, H., Google Inc.: Boosting Sex Identification Performance. IJCV 1(71), 111–119 (2007)CrossRefGoogle Scholar
  10. 10.
    Abdi, H., Valentin, D., Edelman, B., O’Toole, A.: More about the difference between men and women: evidence from linear neural networks and the principal component approach. Perception 24, 539–562 (1995)CrossRefGoogle Scholar
  11. 11.
    O’Toole, A., Adbi, H., Deffenbacher, K., Valentin, D.: A low dimensional representation of faces in the higher dimensions of space. Journal of the Optical Society of America 10, 405–411 (1993)CrossRefGoogle Scholar
  12. 12.
    Smith, W.A.P., Hancock, E.R.: Facial Shape-from-shading and Recognition using Principal Geodesic Analysis and Robust Statistics. International Journal of Computer Vision 76(1), 71–91 (2008)CrossRefGoogle Scholar
  13. 13.
    Devijver, P., Kittler, J.: Pattern Recognition: A Statistical Approach. Prentice Hall, Englewood Cliffs (1982)zbMATHGoogle Scholar
  14. 14.
    Zhu, X., Ghahramani, Z., Lafferty, J.: Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions. In: ICML 2003 (2003)Google Scholar
  15. 15.
    O’Toole, A.J., Vetter, T., Troje, N.F., Bulthoff, H.H.: Sex Classification is Better with Three-Dimensional Structure than with Image Intensity Information. Perception 26, 75–84 (1997)CrossRefGoogle Scholar
  16. 16.
    Pennec, X.: Probabilities and statistics on riemannian manifolds: A geometric approach. Technical Report RR-5093, INRIA (2004)Google Scholar
  17. 17.
    Sirovich, L.: Turbulence and the dynamics of coherent structures. Quart. Applied Mathematics XLV(3), 561–590 (1987)MathSciNetGoogle Scholar
  18. 18.
    Troje, N., Bulthoff, H.H.: Face recognition under varying poses: The role of texture and shape. Vision Research 36, 1761–1771 (1996)CrossRefGoogle Scholar
  19. 19.
    Blanz, V., Vetter, T.: A Morphable Model for the Synthesis of 3D Faces. In: SIGGRAPH 1999 Conference Proceedings, pp. 187–194 (1999)Google Scholar
  20. 20.
    Wu, J., Smith, W.A.P., Hancock, E.R.: Learning Mixture Models for Gender Classification Based on Facial Surface Normals. In: Martí, J., Benedí, J.M., Mendonça, A.M., Serrat, J. (eds.) IbPRIA 2007. LNCS, vol. 4477, pp. 39–46. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  21. 21.
    Martinez, A.M., Benavente, R.: The AR Face Database. CVC Technical Report, 24 (June 1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Jing Wu
    • 1
  • William A. P. Smith
    • 1
  • Edwin R. Hancock
    • 1
  1. 1.Department of Computer ScienceUniversity of YorkYorkUK

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