Effective Detector and Kalman Filter Based Robust Face Tracking System

  • Chi-Young Seong
  • Byung-Du Kang
  • Jong-Ho Kim
  • Sang-Kyun Kim
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4319)


We present a robust face tracking system from the sequence of video images based on effective detector and Kalman filter. To construct the effective face detector, we extract the face features using the five types of simple Haar-like features. Extracted features are reinterpreted using Principal Component Analysis (PCA), and interpreted principal components are used for Support Vector Machine (SVM) that classifies the faces and non-faces. We trace the moving face with Kalman filter, which uses the static information of the detected faces and the dynamic information of changes between previous and current frames. To make a real-time tracking system, we reduce processing time by adjusting the frequency of face detection. In this experiment, the proposed system showed an average tracking rate of 95.5% and processed at 15 frames per second. This means the system is robust enough to track faces in real-time.


Support Vector Machine Kalman Filter Support Vector Machine Classifier Face Detection Current Frame 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Schwerdt, K., Crowley, J.L.: Robust Face Tracking Using Colour. In: IEEE Int’l Conf. Automatic Face and Gesture Recognition, pp. 90–95 (2000)Google Scholar
  2. 2.
    Birchfield, S.: Elliptical Head Tracking using Intensity Gradient and Color Histograms. IEEE Computer Vision and Pattern Recognition, 232–237 (1998)Google Scholar
  3. 3.
    Hager, G., Toyama, K.: X Vision: A Portable Substrate for Real-Time Vision Applications. Computer Vision and Image Understanding 69(1), 23–37 (1998)CrossRefGoogle Scholar
  4. 4.
    Yao, Z., Li, H.: Tracking a Detected Face with Dynamic Programming. Image and Vision Computing 24(6), 573–580 (2006)CrossRefMathSciNetGoogle Scholar
  5. 5.
    Johnson, R.A., Wichern, D.W.: Applied Multivariate Statistical Analysis, pp. 356–395. Prentice-Hall, Englewood Cliffs (2002)Google Scholar
  6. 6.
    Vapnik, V.: The Nature of Statistical Learning Theory, 2nd edn. Springer, Heidelberg (2001)Google Scholar
  7. 7.
    Viola, P., Jones, M.J.: Robust Real-Time Face Detection. International Journal of Computer Vision 57(2), 137–154 (2004)CrossRefGoogle Scholar
  8. 8.
    MIT CBCL - Face Database, http://www.ai.mit.edu/projects/cbcl/
  9. 9.
    Welch, G., Bishop, G.: An Introduction to the Kalman filter. University of North Carolina at Chapel Hill, Department of Computer Science, TR 95-041 (2004)Google Scholar
  10. 10.
  11. 11.
    Boston University IVC Head Tracking Video Set, http://www.cs.bu.edu/groups/ivc/
  12. 12.
    Lienhart, R., Maydt, J.: An Extended Set of Haar-like Features for Rapid Object Detection. In: IEEE Int’l Conf. Image Processing, vol. 1, pp. 900–903 (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Chi-Young Seong
    • 1
  • Byung-Du Kang
    • 1
  • Jong-Ho Kim
    • 1
  • Sang-Kyun Kim
    • 1
  1. 1.Department of Computer ScienceInje UniversityKimhaeKorea

Personalised recommendations