Advertisement

Cascade AdaBoost Classifiers with Stage Optimization for Face Detection

  • Zongying Ou
  • Xusheng Tang
  • Tieming Su
  • Pengfei Zhao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3832)

Abstract

In this paper, we propose a novel feature optimization method to build a cascade Adaboost face detector for real-time applications, such as teleconferencing, user interfaces, and security access control. AdaBoost algorithm selects a set of weak classifiers and combines them into a final strong classifier. However, conventional AdaBoost is a sequential forward search procedure using the greedy selection strategy, the weights of weak classifiers may not be optimized. To address this issue, we proposed a novel Genetic Algorithm post optimization procedure for a given boosted classifier, which yields better generalization performance.

References

  1. 1.
    Yang, M.H., Kriegman, D.J., Ahuja, N.: Detecting Faces in Images: A Survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 24, 34–58 (2002)CrossRefGoogle Scholar
  2. 2.
    Rowly, H., Baluja, S., Kanade, T.: Neural network-based face detection. PAMI 20, 23–38 (1998)Google Scholar
  3. 3.
    Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. IEEE CVPR, 511–518 (2001)Google Scholar
  4. 4.
    Romdhani, S., Torr, P., Schoelkopf, B., Blake, A.: Computationally efficient face detection. In: Proc. Intl. Conf. Computer Vision, pp. 695–700 (2001)Google Scholar
  5. 5.
    Henry, S., Takeo, K.: A statistical model for 3d object detection applied to faces and cars. In: IEEE Conference on Computer Vision and Pattern Recognition (2000)Google Scholar
  6. 6.
    Freund, Y., Schapire, R.: A diction-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences 55, 119–139 (1997)zbMATHCrossRefMathSciNetGoogle Scholar
  7. 7.
    Li, S.Z., Zhang, Z.Q., Harry, S., Zhang, H.J.: FloatBoost learning for classification. In: Proc.CVPR, pp. 511–518 (2001)Google Scholar
  8. 8.
    Lienhart, R., Kuranov, A., Pisarevsky, V.: Empirical analysis of detection cascades of boosted classifiers for rapid object detection. Technical report, MRL, Intel Labs (2002)Google Scholar
  9. 9.
    Viola, P., Jones, M.: Fast and robust classification using asymmetric AdaBoost and a detector cascade. In: NIPS, vol. 14 (2002)Google Scholar
  10. 10.
    Sung, K.K.: Learning and Example Selection for Object and Pattern Detection. PhD thesis, MIT AI Lab (January 1996)Google Scholar
  11. 11.
    Goldberg, D.E.: Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, Reading (1989)zbMATHGoogle Scholar
  12. 12.
    Carbonetto, P.: Viola training data (Database), http://www.cs.ubc.ca/~pcarbo
  13. 13.

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Zongying Ou
    • 1
  • Xusheng Tang
    • 1
  • Tieming Su
    • 1
  • Pengfei Zhao
    • 1
  1. 1.Key Laboratory for Precision and Non-traditional Machining Technology, of Ministry of EducationDalian University of TechnologyDalianP.R. China

Personalised recommendations