Face Detection and Facial Component Extraction by Wavelet Decomposition and Support Vector Machines

  • Dihua Xi
  • Seong-Whan Lee
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2688)


Quite recently the support vector machine (SVM) has shown a great potential in the area of automatic face detection. Generally the SVM based methods fall into two categories: component-based and whole face-based. However there exist some limitations to each category. In this paper we present a two-stage method using both SVM categories based on multiresolution wavelet decomposition (MWD). In the first stage, the whole face-based SVMs are used for coarse location of faces from small sub-images of low resolution. Then a set of component-based SVMs are applied to verify the extracted candidates in subsequent larger sub-images of higher resolutions. Experimental results show that this wavelet-SVM based method takes the advantage of the effectiveness of both categories of SVM-based methods and the computation efficiency.


Support Vector Machine Feature Vector Independent Component Analysis Face Image Face Detection 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Dihua Xi
    • 1
  • Seong-Whan Lee
    • 1
  1. 1.Center for Artificial Vision ResearchKorea UniversitySeoulKorea

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