Face Tracking Using Mean-Shift Algorithm: A Fuzzy Approach for Boundary Detection

  • Farhad Dadgostar
  • Abdolhossein Sarrafzadeh
  • Scott P. Overmyer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3784)


Face and hand tracking are important areas of research, related to adaptive human-computer interfaces, and affective computing. In this article we have introduced two new methods for boundary detection of the human face in video sequences: (1) edge density thresholding, and (2) fuzzy edge density. We have analyzed these algorithms based on two main factors: convergence speed and stability against white noise. The results show that “fuzzy edge density” method has an acceptable convergence speed and significant robustness against noise. Based on the results we believe that this method of boundary detection together with the mean-shift and its variants like cam-shift algorithm, can achieve fast and robust tracking of the face in noisy environment, that makes it a good candidate for use with cheap cameras and real-world applications.


Video Sequence Convergence Speed Fuzzy Controller Noisy Environment Edge Density 
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.
    Bianchi-berthouze, N., Kleinsmith, A.A.: A categorical approach to affective gesture recognition. Journal of Neural Computing, Artificial Intelligence & Cognitive Research 15(4), 259–269Google Scholar
  2. 2.
    Bradski, G.R.: Computer Vision Face Tracking For Use in a Perceptual User Interface, Microcomputer Research Lab, Santa Clara, CA, Intel Corporation (1998)Google Scholar
  3. 3.
    Cheng, Y.: Mean shift, mode seeking, and clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence 17(8), 790–799Google Scholar
  4. 4.
    Collins, R.T.: Mean-shift Blob Tracking through Scale Space. In: IEEE Conference on Computer Vision and Pattern Recognition (2003)Google Scholar
  5. 5.
    Comaniciu, D., Ramesh, V., Meer, P.: Real-Time Tracking of Non-Rigid Objects using Mean Shift. IEEE Computer Vision and Pattern Recognition, 142–149 (2000)Google Scholar
  6. 6.
    Dadgostar, F., Sarrafzadeh, A., Johnson, M.J.: An Adaptive Skin Detector for Video Sequences Based on Optical Flow Motion Features. In: International Conference on Signal and Image Processing (SIP), Hawaii, USA (2005)Google Scholar
  7. 7.
    Dadgostar, F., Sarrafzadeh, A., Overmyer, S.P.: An Adaptive Real-time Skin Detector for Video Sequences. In: International Conference on Computer Vision, Las Vegas, USA (2005)Google Scholar
  8. 8.
    KaewTraKulPong, P., Bowden, R.: An Adaptive Visual System for Tracking Low Resolution Colour Targets. In: BMVC 2001, Manchester, UK, pp. 243–252 (2001)Google Scholar
  9. 9.
    Kapoor, A., Picard, R.W.: A real-time head nod and shake detector. In: Proceedings of the Workshop on Perceptive User Interfaces, Orlando, Florida, USA (2001)Google Scholar
  10. 10.
    Sherrah, J., Gong, S.: Tracking Discontinuous Motion Using Bayesian Inference. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1843, pp. 150–166. Springer, Heidelberg (2000)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Farhad Dadgostar
    • 1
  • Abdolhossein Sarrafzadeh
    • 1
  • Scott P. Overmyer
    • 2
  1. 1.Institute of Information and Mathematical SciencesMassey UniversityAucklandNew Zealand
  2. 2.Department of Electrical Engineering & Computer ScienceSouth Dakota State UniversityBrookingsUSA

Personalised recommendations