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Multimodal emotion recognition from expressive faces, body gestures and speech

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dc.contributor.author Caridakis, G en
dc.contributor.author Castellano, G en
dc.contributor.author Kessous, L en
dc.contributor.author Raouzaiou, A en
dc.contributor.author Malatesta, L en
dc.contributor.author Asteriadis, S en
dc.contributor.author Karpouzis, K en
dc.date.accessioned 2014-03-01T02:44:51Z
dc.date.available 2014-03-01T02:44:51Z
dc.date.issued 2007 en
dc.identifier.issn 15715736 en
dc.identifier.uri https://dspace.lib.ntua.gr/xmlui/handle/123456789/31974
dc.subject Affective body language en
dc.subject Affective speech en
dc.subject Emotion recognition en
dc.subject Multimodal fusion en
dc.title Multimodal emotion recognition from expressive faces, body gestures and speech en
heal.type conferenceItem en
heal.identifier.primary 10.1007/978-0-387-74161-1_41 en
heal.identifier.secondary http://dx.doi.org/10.1007/978-0-387-74161-1_41 en
heal.publicationDate 2007 en
heal.abstract 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. © 2007 International Federation for Information Processing. en
heal.journalName IFIP International Federation for Information Processing en
dc.identifier.doi 10.1007/978-0-387-74161-1_41 en
dc.identifier.volume 247 en
dc.identifier.spage 375 en
dc.identifier.epage 388 en


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