Advertisement

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)

Abstract

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.

Keywords

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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    Yang, M.H., Kriegman, D., Ahuja, N.: Detecting faces in images: A survey. IEEE Trans. on Pattern Analysis and Machine Intelligence 24 (2002) 34–58CrossRefGoogle Scholar
  2. [2]
    Hjelmas, E., Low, B.K.: Face detection: A survey. Computer Vision and Image Understanding 83 (2001) 236–274zbMATHCrossRefGoogle Scholar
  3. [3]
    Vapnik, V. N.: The Nature of Statistical Learning Theory. Springer, New York (1995)zbMATHGoogle Scholar
  4. [4]
    Li, Y., Gong, S., Sherrah, J., Liddell, H.: Multi-view face detection using support vector machines and eigenspace modelling. In: 4th International Conference on Knowledge-Based Intelligent Engineering Systems & Allied Technologies. Volume 1, University of Brighton, UK (2000) 241–244Google Scholar
  5. [5]
    Papageorgiou, C. P., Poggio, T.: A trainable system for object detection. International Journal of Computer Vision 38 (2000) 15–33zbMATHCrossRefGoogle Scholar
  6. [6]
    Osuna, E., Freund, R., Girosi, F.: Training support vector machines: application to face detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Juan, Puerto Rico (1997) 130–136Google Scholar
  7. [7]
    Qi, Y., Doermann, D., DeMenthon, D.: Hybrid independent component analysis and support vector machine learning scheme for face detection. In: IEEE International Conference on Acoustics, Speech, and Signal Processing. Volume 3., Salt Lake City, Utah, US (2001) 1481–1484Google Scholar
  8. [8]
    Mallat, S.: A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans. on Pattern Analysis and Machine Intelligence 11(7) (1989) 674–693zbMATHCrossRefGoogle Scholar
  9. [9]
    Vetter, T., Jones, M. J., Poggio, T.: A bootstrapping algorithm for learning linear models of object classes. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Juan, Puerto Rico (1997) 40–46Google Scholar
  10. [10]
    Hwang, B.-W., Roh, M. C., Byun, H., Lee, S.-W.: Performance evaluation of face recognition algorithms on the asian face database, kfdb. In: 4th International Conference on Audio-and Video-Based Biometric Person Authentication, Guildford, UK (2003)Google Scholar

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

Personalised recommendations