In this paper we present a multimodal approach for the recognition of eight emotions that integrates information from facial expressions, body movement and gestures and speech. We trained and tested a model with a bayesian classifier, using a multimodal corpus with eight emotions and ten subjects. First individual classifiers were trained for each modality. Then data were fused at the feature level and the decision level. Fusing multimodal data increased very much the recognition rates in comparison with the unimodal systems: the multimodal approach gave an improvement of more than 10% with respect to the most successful unimodal system. Further, the fusion performed at the feature level showed better results than the one performed at the decision level.keywordsaffective body languageaffective speechemotion recognitionmultimodal fusion.
|Title of host publication||Artificial Intelligence and Innovations 2007: from Theory to Applications|
|Subtitle of host publication||Proceedings of the 4th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI 2007)|
|Number of pages||14|
|Publication status||Published - 2007|
|Series||IFIP Advances in Information and Communication Technology|