Facial Expression Classification Based on Dempster-Shafer Theory of Evidence

  • Mohammad Shoyaib
  • M. Abdullah-Al-Wadud
  • S. M. Zahid Ishraque
  • Oksam Chae
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 164)

Abstract

Facial expression recognition is a well discussed problem. Several machine learning methods are used in this regard. Among them, Adaboost is popular for its simplicity and considerable accuracy. In Adaboost, decisions are made based on the weighted majority vote of several weak classifiers. However, such weighted combination may not give expected accuracy due to the lack of proper uncertainty management. In this paper, we propose to adopt the Dempster Shafer theory (DST) of Evidence based solution where mass values are calculated from k-nearest neighboring feature information based on some distance metric, and combined together using DST. Experiments on a renowned dataset demonstrate the effectiveness of the proposed method.

Keywords

Facial Expression Expression Recognition Facial Expression Recognition Weak Classifier Dempster Shafer Theory 
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 2012

Authors and Affiliations

  • Mohammad Shoyaib
    • 1
  • M. Abdullah-Al-Wadud
    • 2
  • S. M. Zahid Ishraque
    • 1
  • Oksam Chae
    • 1
  1. 1.Kyung Hee UniversityYonginKorea
  2. 2.Hankuk University of Foreign StudiesYonginKorea

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