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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 unrelia-bility of labels, variations of the same affective states amongst different persons and in different modalities as well as the presence of(More)
Systems for the recognition of psychological characteristics such as the emotional state in real world scenarios have to deal with several difficulties. Amongst those are unconstrained environments and uncertainties in one or several input channels. However a more crucial aspect is the content of the data itself. Psychological states are highly(More)
Multimodal emotion recognition in real world environments is still a challenging task of affective computing research. Recognizing the affective or physiological state of an individual is difficult for humans as well as for computer systems, and thus finding suitable discriminative features is the most promising approach in multimodal emotion recognition.(More)
A lot of research effort has been spent on the development of emotion theories and modeling, however, their suitability and applicability to expressions in human computer interaction has not exhaustively been evaluated. Furthermore, investigations concerning the ability of the annotators to map certain expressions onto the developed emotion models is(More)
The focus of this work is emotion recognition in the wild based on a multitude of different audio, visual and meta features. For this, a method is proposed to optimize multi-modal fusion architectures based on evolutionary computing. Extensive uni- and multi-modal experiments show the discriminative power of each computed feature set and fusion(More)