Multimodal affect recognition in spontaneous HCI environment

Human Computer Interaction (HCI) is known to be a multimodal process. In this paper we will show results of experiments for affect recognition, with non-acted, affective multimodal data from the new Last Minute Corpus (LMC). This corpus is more related to real HCI applications than other known data sets where affective behavior is elicited untypically for HCI.We utilize features from three modalities: facial expressions, prosody and gesture. The results show, that even simple fusion architectures can reach respectable results compared to other approaches. Further we could show, that probably not all features and modalities contribute substantially to the classification process, where prosody and eye blink frequency seem most contributing in the analyzed dataset.