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Classifier Fusion Applied to Facial Expression Recognition: An Experimental Comparison

  • Martin Schels
  • Christian Thiel
  • Friedhelm Schwenker
  • Günther Palm
Part of the Cognitive Systems Monographs book series (COSMOS, volume 6)

Abstract

In this paper classifier fusion approaches are investigated through numerical evaluation. For this purpose a multi classifier architecture for the recognition of human facial expressions in image sequences has been constructed on characteristic facial regions and three different feature types (principal components, orientation histograms of static images and temporal features based on optical flow). Classifier fusion is applied to the individual channels established by feature principle and facial region, which are addressed to by individual classifiers. The available combinations of classifier outputs are examined and it is investigated how combining classifiers can lead to more appropriate results. The stability of fusion regarding varying classifier combinations is studied and the fused classifier output is compared to the human view on the data.

Keywords

Facial Expression Recognition Rate Confusion Matrix Gesture Recognition Fusion Rule 
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 2009

Authors and Affiliations

  • Martin Schels
    • 1
  • Christian Thiel
    • 1
  • Friedhelm Schwenker
    • 1
  • Günther Palm
    • 1
  1. 1.Institute of Neural Information ProcessingUniversity of UlmUlmGermany

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