Skip to main content
  • Research Article
  • Open access
  • Published:

Spatio-Temporal Graphical-Model-Based Multiple Facial Feature Tracking


It is challenging to track multiple facial features simultaneously when rich expressions are presented on a face. We propose a two-step solution. In the first step, several independent condensation-style particle filters are utilized to track each facial feature in the temporal domain. Particle filters are very effective for visual tracking problems; however multiple independent trackers ignore the spatial constraints and the natural relationships among facial features. In the second step, we use Bayesian inference—belief propagation—to infer each facial feature's contour in the spatial domain, in which we learn the relationships among contours of facial features beforehand with the help of a large facial expression database. The experimental results show that our algorithm can robustly track multiple facial features simultaneously, while there are large interframe motions with expression changes.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Congyong Su.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License ( ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Reprints and permissions

About this article

Cite this article

Su, C., Huang, L. Spatio-Temporal Graphical-Model-Based Multiple Facial Feature Tracking. EURASIP J. Adv. Signal Process. 2005, 215497 (2005).

Download citation

  • Received:

  • Revised:

  • Published:

  • DOI:

Keywords and phrases: