Classifier Fusion Applied to Facial Expression Recognition: An Experimental Comparison
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.
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