Active Learned Multi-view Face Detection Tree Using Fuzzy Cluster Validity Analysis

  • Chunna Tian
  • Xinbo Gao
  • Jie Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4223)


An active learned face detection tree based on FloatBoost method is proposed to accommodate the in-class variability of multi-view faces. To handle the computation resource constraints to the size of training example set, an embedded Bootstrap example selection algorithm is proposed, which leads to a more effective predictor. The tree splitting procedure is realized through dividing face training examples into the optimal sub-clusters using the fuzzy c-means algorithm together with a new cluster validity function based on the modified partition fuzzy degree. Then each sub-cluster of face examples is conquered with the FloatBoost learning to construct branches in the node of the detection tree. During training, the proposed algorithm is much faster than the original detection tree. The experimental results illustrate that the proposed detection tree is more efficient than the original one while keeping its detection speed. And the E-Bootstrap strategy outperforms the Bootstrap one in selecting relevant examples.


Face Detection Weak Classifier Cluster Validity Positive Training Negative Training 
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.
    Yang, M.H., Kriegman, D.J., Ahuja, N.: Detecting Faces in Images: A Survey. IEEE Trans. on Pattern Analysis and Machine Intelligence 24, 34–58 (2002)CrossRefGoogle Scholar
  2. 2.
    Lanitis, A., Taylor, C.J., Cootes, T.F.: An Automatic Face Identification System Using Flexible Appearance Models. Image and Vision Computing 13, 393–401 (1995)CrossRefGoogle Scholar
  3. 3.
    Sung, K.K., Poggio, T.: Example-based Learning for View-based Human Face Detection. IEEE Trans. on Pattern Analysis and Machine Intelligence 20, 39–51 (1998)CrossRefGoogle Scholar
  4. 4.
    Viola, P., Jones, M.: Rapid Object Detection Using a Boosted Cascade of Simple Features. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition, Kauai, Hawaii USA, vol. 1, pp. 511–518 (2001)Google Scholar
  5. 5.
    Li, S.Z., Zhang, Z.Q.: Float Boost Learning and Statistical Face Detection. IEEE Trans. on Pattern Analysis and Machine Intelligence 26, 1112–1122 (2004)CrossRefGoogle Scholar
  6. 6.
    Lienhart, R., Liang, L.H., Kuranov, A.: A Detector Tree of Boosted Classifiers for Real-time Object Detection and Tracking. In: IEEE Conf. International Conference on Multimedia and Expo, Baltimore, MD, USA, vol. 2, pp. 6–9 (2003)Google Scholar
  7. 7.
    Li, J., Gao, X.B., Jiao, L.C.: A New Cluster Validity Function Based on the Modified Partition Fuzzy Degree. In: Tsumoto, S., Słowiński, R., Komorowski, J., Grzymała-Busse, J.W. (eds.) RSCTC 2004. LNCS (LNAI), vol. 3066, pp. 586–591. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  8. 8.
    Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York (1981)MATHGoogle Scholar
  9. 9.
  10. 10.
    Gao, X.B., Tang, X.: Unsupervised Video Shot Segmentation and Model-free Anchorperson Detection for News Video Story Parsing. IEEE Trans. on Circuits Systems for Video Technology 12, 765–776 (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Chunna Tian
    • 1
  • Xinbo Gao
    • 1
  • Jie Li
    • 1
  1. 1.School of Electronic EngineeringXidian UniversityXi’anChina

Personalised recommendations