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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)

Abstract

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.

Keywords

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.

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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

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