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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, different realizations 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 with 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 input signals to a reference time series in the form of ground truth labels.

Fusion of Audio-visual Features using Hierarchical Classifier Systems for the Recognition of Affective States and the State of Depression