Recognizing Facial Expression Using Particle Filter Based Feature Points Tracker

  • Rakesh Tripathi
  • R. Aravind
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4815)


The paper focuses on an evaluation of particle filter based facial feature tracker. Particle filter is a successful tool in the non-linear and the non-Gaussian estimation problems. We developed a particle filter based facial points tracker with a simple observation model based on sum-of-squared differences (SSD) between the intensities. Multistate face component model is used to estimate the occluded feature points. The important distances are calculated from tracked points. Two kinds of classification schemes are considered, the hidden Markov model (HMM) as sequence based recognizer and support vector machine (SVM) as frame based recognizer. A comparative study is shown in the classification of five basic expressions, i.e., anger, sadness, happiness, surprise and disgust. The tests are conducted on Cohn-Kanade and MMI face expression databases.


Support Vector Machine Facial Expression Hide Markov Model Feature Point Recognition Rate 
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 2007

Authors and Affiliations

  • Rakesh Tripathi
    • 1
  • R. Aravind
    • 1
  1. 1.Department of Electrical Engineering, Indian Institute of Technology Madras, Chennai-36India

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