Authors:
Markus Kächele
;
Michael Glodek
;
Dimitrij Zharkov
;
Sascha Meudt
and
Friedhelm Schwenker
Affiliation:
Ulm University, Germany
Keyword(s):
Emotion Recognition, Multiple Classifier Systems, Affective Computing, Information Fusion.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Audio and Speech Processing
;
Computer Vision, Visualization and Computer Graphics
;
Digital Signal Processing
;
Ensemble Methods
;
Image and Video Analysis
;
Multiclassifier Fusion
;
Multimedia
;
Multimedia Signal Processing
;
Pattern Recognition
;
Software Engineering
;
Telecommunications
;
Theory and Methods
;
Video Analysis
Abstract:
Reliable prediction of affective states in real world scenarios is very challenging and a significant amount of
ongoing research is targeted towards improvement of existing systems. Major problems include the unreliability
of labels, variations of the same affective states amongst different persons and in different modalities
as well as the presence of sensor noise in the signals. This work presents a framework for adaptive fusion of
input modalities incorporating variable degrees of certainty on different levels. Using a strategy that starts with
ensembles of weak learners, gradually, level by level, the discriminative power of the system is improved by
adaptively weighting favorable decisions, while concurrently dismissing unfavorable ones. For the final decision
fusion the proposed system leverages a trained Kalman filter. Besides its ability to deal with missing and
uncertain values, in its nature, the Kalman filter is a time series predictor and thus a suitable choice to match
i
nput signals to a reference time series in the form of ground truth labels. In the case of affect recognition, the
proposed system exhibits superior performance in comparison to competing systems on the analysed dataset.
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