Gender Classification of Faces Using Adaboost

  • Rodrigo Verschae
  • Javier Ruiz-del-Solar
  • Mauricio Correa
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4225)


In this work it is described a framework for classifying face images using Adaboost and domain-partitioning based classifiers. The most interesting aspect of this framework is the capability of building classification systems with high accuracy in dynamical environments, which achieve, at the same time, high processing and training speed. We apply this framework to the specific problem of gender classification. We built several gender classification systems under the proposed framework using different features (LBP, wavelets, rectangular, etc.). These systems are analyzed and evaluated using standard face databases (FERET and BioID), and a new gender classification database of real-world images.


  1. 1.
    Buchala, S., Davey, N., Frank, R.J., Gale, T.M., Loomes, M., Kanargard, W.: Gender Classification of Face 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
  2. 2.
    Fröba, B., Ernst, A.: Face detection with the modified census transform. In: 6th Int. Conf. on Face and Gesture Recognition - FG 2004, Seoul, Korea, May 2004, pp. 91–96 (2004)Google Scholar
  3. 3.
    Delakis, M., Garcia, C.: Convolutional face finder: A neural architecture for fast and robust face detection. IEEE Trans. Pattern Anal. Mach. Intell. 26(11), 1408–1423 (2004)CrossRefGoogle Scholar
  4. 4.
    Gao, W., Cao, B., Shan, S., Zhou, D., Zhang, X., Zhao, D.: The CAS-PEAL Large-Scale Chinese Face Database and Evaluation Protocols, Technical Report No. JDL_TR_04_FR_001, Joint Research & Development Laboratory, CAS (2004)Google Scholar
  5. 5.
    Moghaddam, B., Yang, M.-H.: Learning Gender with Support Faces. IEEE Trans. Pattern Anal. Mach. Intell. 24(5), 707–711 (2002)CrossRefGoogle Scholar
  6. 6.
    Phillips, P.J., Wechsler, H., Huang, J., Rauss, P.: The FERET database and evaluation procedure for face recognition algorithms. Image and Vision Computing J. 16(5), 295–306 (1998)CrossRefGoogle Scholar
  7. 7.
    Schapire, R.E.: A brief introduction to boosting. In: Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence (1999)Google Scholar
  8. 8.
    Schapire, R.E., Singer, Y.: Improved Boosting Algorithms using Confidence-rated Predictions. Machine Learning 37(3), 297–336 (1999)zbMATHCrossRefGoogle Scholar
  9. 9.
    Schneidermann, H., Kanade, T.: A statistical model for 3D object detection applied to faces and cars. In: IEEE Conf. on Computer Vision and Pattern Recognition, vol. 1, pp. 746–751 (2000)Google Scholar
  10. 10.
    Shakhnarovich, G., Viola, P., Moghaddam, B.: A Unified Learning Framework for Real Time Face Detection & Classification. In: Int. Conf. on Automatic Face & Gesture Recognition – FG 2002, May 2002, pp. 16–26 (2002)Google Scholar
  11. 11.
    Verschae, R., Ruiz-del-Solar, J., Correa, M., Vallejos, P.: A Unified Learning Frame-work for Face, Eyes and Gender Detection using Nested Cascades of Boosted Classifiers, Technical Report UCH-DIE-VISION-2006-02, Dept. of E. Eng., U. de Chile (2006)Google Scholar
  12. 12.
    Viola, P., Jones, M.: Fast and robust classification using asymmetric adaboost and a detector cascade. Advances in Neural Inform. Processing System 14. MIT Press, Cambridge (2002)Google Scholar
  13. 13.
    Wu, B., Ai, H., Huang, C., Lao, S.: Fast rotation invariant multi-view face detection based on real Adaboost. In: 6th Int. Conf. on Face and Gesture Recognition - FG 2004, Seoul, Korea, May 2004, pp. 79–84 (2004)Google Scholar
  14. 14.
    Wu, B., Ai, H., Huang, C.: LUT-based Adaboost for Gender Classification. In: 4th Int. Conf. on Audio and Video-based Biometric Person Authentication, Guildford, United Kingdom, June 10-11 (2003)Google Scholar
  15. 15.
    BioID Face Database. Available on april 2006 in:

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Rodrigo Verschae
    • 1
    • 2
    • 3
  • Javier Ruiz-del-Solar
    • 1
    • 2
  • Mauricio Correa
    • 1
    • 2
  1. 1.Department of Electrical EngineeringUniversidad de ChileChile
  2. 2.Center for Web Research, Department of Computer ScienceUniversidad de ChileChile
  3. 3.CMLAENS CachanFrance

Personalised recommendations