Sascha Meudt

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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, 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)
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)
In recent years the fields of affective computing and emotion recognition have experienced a steady increase in attention and especially the creation and analysis of multi-modal corpora has been the focus of intense research. Plausible annotation of this data, however is an enormous problem. In detail emotion annotation is very time consuming, cumbersome(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)