Empirical Analysis of Detection Cascades of Boosted Classifiers for Rapid Object Detection

  • Rainer Lienhart
  • Alexander Kuranov
  • Vadim Pisarevsky
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2781)


Recently Viola et al. have introduced a rapid object detection scheme based on a boosted cascade of simple feature classifiers. In this paper we introduce and empirically analysis two extensions to their approach: Firstly, a novel set of rotated haar-like features is introduced. These novel features significantly enrich the simple features of [6] and can also be calculated efficiently. With these new rotated features our sample face detector shows off on average a 10% lower false alarm rate at a given hit rate. Secondly, we present a through analysis of different boosting algorithms (namely Discrete, Real and Gentle Adaboost) and weak classifiers on the detection performance and computational complexity. We will see that Gentle Adaboost with small CART trees as base classifiers outperform Discrete Adaboost and stumps. The complete object detection training and detection system as well as a trained face detector are available in the Open Computer Vision Library at [8].


False Alarm False Alarm Rate Face Detector Actual Face Weak Classifier 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Freund, Y., Schapire, R.E.: Experiments with a new boosting algorithm. In: Machine Learning: Proceedings of the Thirteenth International Conference, pp. 148–156. Morgan Kauman, San Francisco (1996)Google Scholar
  2. 2.
    Li, S.Z., Zhu, L., Zhang, Z., Blake, A., Zhang, H., Shum, H.: Statistical Learning of Multi-View Face Detection. In: Proceedings of The 7th European Conference on Computer Vision, Copenhagen, Denmark (May 2002)Google Scholar
  3. 3.
    Lienhart, R., Maydt, J.: An Extended Set of Haar-like Features for Rapid Object Detection. In: IEEE ICIP 2002, September 2002, vol. 1, pp. 900–903 (2002)Google Scholar
  4. 4.
    Mohan, A., Papageorgiou, C., Poggio, T.: Example-based object detection in images by components. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(4), 349–361 (2001)CrossRefGoogle Scholar
  5. 5.
    Papageorgiou, C., Oren, M., Poggio, T.: A general framework for Object Detection. In: International Conference on Computer Vision (1998)Google Scholar
  6. 6.
    Viola, P., Jones, M.J.: Rapid Object Detection using a Boosted Cascade of Simple Features. In: IEEE CVPR (2001)Google Scholar
  7. 7.
    Rowley, H., Baluja, S., Kanade, T.: Neural network-based face detection. IEEE Patt. Anal. Mach. Intell. 20, 22–38 (1998)Google Scholar
  8. 8.

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Rainer Lienhart
    • 1
  • Alexander Kuranov
    • 1
  • Vadim Pisarevsky
    • 1
  1. 1.Microprocessor Research LabIntel Labs Intel CorporationSanta ClaraUSA

Personalised recommendations