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Face Detection Based on Cost-Gentle Adaboost Algorithm

  • Jian Cheng
  • Haijun Liu
  • Jian Wang
  • Hongsheng Li
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 246)

Abstract

With the development of information technology, the research on face detection has been an important topic in computer vision. In this paper, a novel method is proposed for face detection based on Cost-Gentle Adaboost algorithm. The main differences between our method and the traditional Gentle Adaboost are that the cost factors have been introduced into the training process: the higher the value, the more important of this class samples. In the new training process, the selected classifiers can more effectively focus on the face samples than the traditional Gentle Adaboost algorithm. The face detector trained by our method can achieve higher detection rate at appropriate false positive rates. Experimental results also show that our method is effective.

Keywords

Face detection Cost factors Gentle Adaboost algorithm 

Notes

Acknowledgments

This work is supported by the National Natural Science Foundation of China under Projects 61201271 and Specialized Research Fund for the Doctoral Program of Higher Education 20100185120021.

References

  1. 1.
    Viola P, Jones MJ (2004) Robust real-time face detection. Int J Comput Vis 57(2):137–154CrossRefGoogle Scholar
  2. 2.
    Viola P, Jones M (2001) Rapid object detection using a boosted cascade of simple features. IEEE Comput Soc Conf Comput Vis Pattern Recogn 1:511–518Google Scholar
  3. 3.
    Freund Y, Schapire RE (1997) A decision-theoretic generalization of on-line learning and an application to boosting. J Comp Syst Sci 55(1):119–139CrossRefMATHMathSciNetGoogle Scholar
  4. 4.
    Freund Y, Schapire RE (1996) Experiments with a new boosting algorithm. In: Int Conf Mach Learn., pp 148–156Google Scholar
  5. 5.
    Viola P, Jones M (2001) Fast and robust classification using asymmetric adaboost and a detector cascade. Adv Neural Inform Process Syst 14Google Scholar
  6. 6.
    Ma Y, Ding X (2003) Robust real-time face detection based on cost-sensitive adaboost method. In: Proceeding of the IEEE international conference on multimedia and expo, vol 1. pp 465–468Google Scholar
  7. 7.
    Hou X, Liu CL, Tan T (2006) Learning boosted asymmetric classifiers for object detection. In: Proceeding of the IEEE conference on computer vision and pattern recognition, vol 1. pp 330–338Google Scholar
  8. 8.
    Friedman J, Hastie T, Tibshirani R (2000) Additive logistic regression: a statistical view of boosting. Ann Stat 28(2):337–407CrossRefMATHMathSciNetGoogle Scholar
  9. 9.
    Lienhart R, Maydt J (2002) An extended set of haar-like features for rapid object detection. In: Proceeding of the international conference on image processing, vol 1. pp 900–903Google Scholar
  10. 10.
    Xue L, Liu Z (2012) Using skin color and HAD-AdaBoost algorithm for face detection in color images. Proceedings of the 2012 National Conference on Information Technology and Computer ScienceGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Jian Cheng
    • 1
  • Haijun Liu
    • 1
  • Jian Wang
    • 1
  • Hongsheng Li
    • 1
  1. 1.School of Electronic EngineeringUniversity of Electronic Science and Technology of ChinaChengduChina

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