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Fast Face Detection Integrating Motion Energy into a Cascade-Structured Classifier

  • Yafeng Deng
  • Guangda Su
  • Jun Zhou
  • Bo Fu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3930)

Abstract

In this paper, we propose a fast and robust face detection method. We train a cascade-structured classifier with boosted haar-like features which uses intensity information only. To speed up the process, we integrate motion energy into the cascade-structured classifier. Motion energy can represent moving the extent of the candidate regions, which is used to reject most of the candidate windows and thus accelerates the evaluation procedure. According to the face presence situation, we divide the system state into three modes, and process input images with an intensity detector, or motion integrated dynamic detector, or else keep the pre-results. Since motion energy can be computed efficiently, processing speed is greatly accelerated. Furthermore, without depending on any supposed motion model, the system is very robust in real situations without the limitation of moving patterns including speed and direction.

Keywords

Input Image Face Detection Recording Image Motion Image Integral Image 
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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References

  1. 1.
    Viola, P., Jones, M.: Robust real time object detection. In: IEEE ICCV Workshop on Statistical and Computational Theories of Vision, Vancouver, Canada, July 13 (2001)Google Scholar
  2. 2.
    Rowley, H., Baluja, S., Kanade, T.: Neural network-based face detection. IEEE Patt. Anal. Mach. Intell. 20, 22–38 (1998)Google Scholar
  3. 3.
    Lienhart, R., Kuranov, A., Pisarevsky, V.: Empirical analysis of detection cascades of boosted classifiers for rapid object detection. In: Michaelis, B., Krell, G. (eds.) DAGM 2003. LNCS, vol. 2781, pp. 297–304. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  4. 4.
    Yang, M.-H., Kriegman, D., Ahuja, N.: Detecting Faces in Images: A Survey. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) 24(1), 34–58 (2002)CrossRefGoogle Scholar
  5. 5.
    Hjelm, E., Low, B.K.: Face Detection: A Survey. Computer Vision and Image Understanding 83(3), 236–274 (2001)CrossRefGoogle Scholar
  6. 6.
    Mikolajczyk, K., Choudhury, R., Schmid, C.: Face detection in a video sequence - a temporal approach. Computer Vision and Pattern Recognition (December 2001)Google Scholar
  7. 7.
    Friedman, J., Hastie, T., Tibshirani, R.: Additive logistic regression a statistical view of boosting, Technical Report, Stanford University (1998)Google Scholar
  8. 8.
    Schapire, E., Singer, Y.: Improved boosting algorithms using confidence-rated predictions. In: Proceedings of the Eleventh Annual Conference on Computational Learning Theory, pp. 80–91 (1998)Google Scholar
  9. 9.
    Li, S.Z., Zhang, Z.Q., Shum, H., Zhang, H.J.: FloatBoost learning for classification. NIPS 15 (December 2002)Google Scholar
  10. 10.
    Elgammal, R.D., Harwood, D., Davis, L.S.: Background and Foreground Modeling using Non-parametric Kernel Density Estimation for Visual Surveillance. Proceedings of the IEEE 90(7) (July 2002)Google Scholar
  11. 11.
    McKenna, S., Gong, S., Collins, J.: Face tracking and pose representation. In: British Machine Vision Conference, Edinburgh (1996)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yafeng Deng
    • 1
  • Guangda Su
    • 1
  • Jun Zhou
    • 1
  • Bo Fu
    • 1
  1. 1.Department of Electronic EngineeringTsinghua UniversityBeijingChina

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