On the Complementarity of Face Parts for Gender Recognition

  • Yasmina Andreu
  • Ramón A. Mollineda
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5197)


This paper evaluates the expected complementarity between the most prominent parts of the face for the gender recognition task. Given the image of a face, five important parts (right and left eyes, nose, mouth and chin) are extracted and represented as appearance-based data vectors. In addition, the full face and its internal rectangular region (excluding hair, ears and contour) are also coded. Several mixtures of classifiers based on (subsets of) these five single parts were designed using simple voting, weighted voting and other learner as combiners. Experiments using the FERET database prove that ensembles perform significantly better than plain classifiers based on single parts (as expected).


Gender recognition Face image analysis Classifier ensembles 


  1. 1.
    Bruce, V., Burton, A., Hanna, E., Healey, P., Mason, O., Coombes, A., Fright, R., Linney, A.: Sex discrimination: how do we tell the difference between male and female faces. Perception 22(2), 131–152 (1993)CrossRefGoogle Scholar
  2. 2.
    Burton, A., Bruce, V., Dench, N.: What’s the difference between men and women? evidence from facial measurement. Perception 22(2), 153–176 (1993)CrossRefGoogle Scholar
  3. 3.
    Wu, J., Smith, W., Hancock, E.: 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
  4. 4.
    Lapedriza, A., Marín-Jiménez, M., Vitrià, J.: Gender recognition in non controlled environments. In: Proc. of 18th International Conference on Pattern Recognition (ICPR 2006), Hong Kong. IEEE, Los Alamitos (2006)Google Scholar
  5. 5.
    Moghaddam, B., Yang, M.: Learning gender with support faces. IEEE Transactions on PAMI 24(5), 707–711 (2002)CrossRefGoogle Scholar
  6. 6.
    Kawano, T., Kato, K., Yamamoto, K.: A comparison of the gender differentiation capability between facial parts. In: Proc. of 17th International Conference on Pattern Recognition (ICPR 2006), Cambridge, U.K. IEEE, Los Alamitos (2004)Google Scholar
  7. 7.
    Buchala, S., Davey, N., Frank, R., Gale, T., Loomes, M., Kanargard, W.: Gender classification of faces images: The role of global and feature-based information. In: Pal, N.R., Kasabov, N., Mudi, R.K., Pal, S., Parui, S.K. (eds.) ICONIP 2004. LNCS, vol. 3316, pp. 763–768. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  8. 8.
    Andreu, Y., Mollineda, R.: The role of face parts in gender recognition. In: Campilho, A., Kamel, M. (eds.) ICIAR 2008. LNCS, vol. 5112, pp. 945–954. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  9. 9.
    Phillips, H., Moon, P., Rizvi, S.: The FERET evaluation methodology for face recognition algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(10) (2000)Google Scholar
  10. 10.
    Cortes, C., Vapnik, V.: Support-vector networks. Machine Learning 20(3), 273–297 (1995)zbMATHGoogle Scholar
  11. 11.
    Martinez, A., Benavente, R.: The AR face database. Technical Report 24, CVC (June 1998)Google Scholar
  12. 12.
    Jesorsky, O., Kirchberg, K., Frischholz, R.: Robust face detection using the hausdorff distance. In: Bigun, J., Smeraldi, F. (eds.) AVBPA 2001. LNCS, vol. 2091, pp. 90–95. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  13. 13.
    Messer, K., Matas, J., Kittler, J., Lüttin, J., Maitre, G.: XM2VTSDB: The extended M2VTS database. In: Audio and Video-based Biometric Person Authentication. AVBPA 1999, pp. 72–77 (1999)Google Scholar
  14. 14.
    Nainia, F.B., Mossb, J.P., Gillc, D.S.: The enigma of facial beauty: Esthetics, proportions, deformity, and controversy. American Journal of Orthodontics and Dentofacial Orthopedics 130(3), 277–282 (2006)CrossRefGoogle Scholar
  15. 15.
    Oguz, O.: The proportion of the face in younger adults using the thumb rule of Leonardo da Vinci. Surgical and Radiologic Anatomy 18(2), 111–114 (1996)CrossRefGoogle Scholar
  16. 16.
    Duin, R., Juszczak, P., Paclik, P., Pekalska, E., de Ridder, D., Tax, J.D.M.: PRTools4, A Matlab Toolbox for Pattern Recognition, 4.0th edn. Delft University of Technology, The Netherlands (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Yasmina Andreu
    • 1
  • Ramón A. Mollineda
    • 1
  1. 1.Dept. Llenguatges i Sistemes InformàticsUniversitat Jaume I.Castelló de la PlanaSpain

Personalised recommendations