On the Combination of Different Template Matching Strategies for Fast Face Detection

  • Bernhard Fröba
  • Walter Zink
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2096)


Computer-based face perception is becoming increasingly important for many applications like biometric face recognition, video coding or multi-model human-machine interaction. Fast and robust detection and segmentation of a face in an unconstrained visual scene is a basic requirement for all kinds of face perception. This paper deals with the integration of three simple visual cues for the task of face detection in grey level images. It is achieved by a combination of edge orientation matching, hough transform and an appearance based detection method. The proposed system is computationally efficient and has proved to be robust under a wide range of acquisition conditions like varying lighting, pixel noise and other image distortions. The detection capabilities of the presented algorithm are evaluated on a large database of 13122 images including the frontal-face set of the m2vts database. We achieve a detection rate of over 91% on this database while having only few false detects at the same time.


Face Image Gesture Recognition Edge Orientation Edge Strength Face Position 
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.
    J.-C. Terrillon, M. David, and S. Akamatsu, “Automatic detection of human faces in natural scene images by use of a skin color model and of invariant moments,” in International Conference on Face and Gesture Recognition, pp. 112–117, 1998.Google Scholar
  2. 2.
    J. Yang, W. Lu, and A. Waibel, “Skin-color modelling and adaption,” in ACCV’98, 1998.Google Scholar
  3. 3.
    Q. Sun, W. Huang, and J. Wu, “Face detection based on color and local symmetry information,” in International Conference on Face and Gesture Recognition, pp. 130–135, 1998.Google Scholar
  4. 4.
    D. Chai and K. N. Ngan, “Locating facial region of a head-and-shoulder color image,” in International Conference on Face and Gesture Recognition, pp. 124–129, 1998.Google Scholar
  5. 5.
    S. McKenna, S. Gong, and Y. Raja, “Face recognition in dynamic scenes,” in British Machine Vision Conference, no. 12, 1997.Google Scholar
  6. 6.
    H. A. Rowley, S. Baluja, and T. Kanade, “Neural network-based face detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20,no. 1, pp. 23–38, 1998.CrossRefGoogle Scholar
  7. 7.
    B. Fasel, “Fast multi-scale face detection,” IDIAP-COM 4, IDIAP, 1998.Google Scholar
  8. 8.
    H. Schneiderman, A Statistical Approach to 3D Object Detection Applied to Faces and Cars. PhD thesis, Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, May 2000.Google Scholar
  9. 9.
    M.-H. Yang, D. Roth, and N. Ahuja, “A snow-based face detector,” in Advances in Neural Information Processing Systems 12 (NIPS 12), pp. 855–861, MIT Press, 2000.Google Scholar
  10. 10.
    K. R. Castleman, Digital Image Processing. Prentice Hall, 1996.Google Scholar
  11. 11.
    B. Fröba and C. Köblbeck, “Face detection and tracking using edge orientation information,” in SPIE Photonics West, 2001.Google Scholar
  12. 12.
    D. Maio and D. Maltoni, “Real-time face location on gray-scale static images,” Pattern Recognition, vol. 33, pp. 1525–1539, September 2000.CrossRefGoogle Scholar
  13. 13.
    R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classufucation. New York: John Wiley & Sons, 2001.Google Scholar
  14. 14.
    K. K. Sung, Learning and Example Seletion for Object and Pattern Detection. PhD thesis, Massachusetts Institute of Technology, January 1996.Google Scholar
  15. 15.
    K. Messer, J. Matas, J. Kittler, J. Luettin, and G. Maitre, “Xm2vtsdb: The extended m2vts database,” in Second International Conference on Audio-and Video-based Biometric Person Authentication, pp. 71–77, 1999.Google Scholar
  16. 16.
    M. Bichsel, Strategies of Robust Object Recognition for the Automatic Identification of Human Faces. PhD thesis, Eidgenössische Technische Hochschule Zürich, Zürich, 1991.Google Scholar
  17. 17.
    M. Burl and P. Perona, “Recognition of planar object classes,” in Proc. CVPR’96, 1996.Google Scholar
  18. 18.
    R. Feraud, O. J. Bernier, J.-E. Viallet, and M. Collobert, “A fast and accurate face detector based on neural networks,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, pp. 42–53, January 2001.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Bernhard Fröba
    • 1
  • Walter Zink
    • 1
  1. 1.Departement of Applied ElectronicsFraunhofer Institute for Integrated CircuitsErlangenGermany

Personalised recommendations