Emotion recognition through multiple modalities: Face, body gesture, speech

Castellano, G; Kessous, L; Caridakis, G

dc.contributor.author	Castellano, G	en
dc.contributor.author	Kessous, L	en
dc.contributor.author	Caridakis, G	en
dc.date.accessioned	2014-03-01T02:45:15Z
dc.date.available	2014-03-01T02:45:15Z
dc.date.issued	2008	en
dc.identifier.issn	03029743	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/32231
dc.subject	Affective body language	en
dc.subject	Affective speech	en
dc.subject	Emotion recognition	en
dc.subject	Multimodal fusion	en
dc.subject.other	Classification (of information)	en
dc.subject.other	Classifiers	en
dc.subject.other	Gesture recognition	en
dc.subject.other	Human computer interaction	en
dc.subject.other	Human engineering	en
dc.subject.other	Information management	en
dc.subject.other	Knowledge management	en
dc.subject.other	Learning systems	en
dc.subject.other	Speech	en
dc.subject.other	Speech recognition	en
dc.subject.other	Affective body language	en
dc.subject.other	Affective speech	en
dc.subject.other	Bayesian classifiers	en
dc.subject.other	Body gesture	en
dc.subject.other	Body movements	en
dc.subject.other	Decision levels	en
dc.subject.other	Emotion recognition	en
dc.subject.other	Facial expressions	en
dc.subject.other	Feature-level	en
dc.subject.other	Individual classifiers	en
dc.subject.other	Multi-modal	en
dc.subject.other	Multi-modal approach	en
dc.subject.other	Multi-modal data	en
dc.subject.other	Multimodal fusion	en
dc.subject.other	Multiple modalities	en
dc.subject.other	Recognition rates	en
dc.subject.other	Unimodal	en
dc.subject.other	Face recognition	en
dc.title	Emotion recognition through multiple modalities: Face, body gesture, speech	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1007/978-3-540-85099-1_8	en
heal.identifier.secondary	http://dx.doi.org/10.1007/978-3-540-85099-1_8	en
heal.publicationDate	2008	en
heal.abstract	In this paper we present a multimodal approach for the recognition of eight emotions. Our approach 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. Firstly, individual classifiers were trained for each modality. Next, data were fused at the feature level and the decision level. Fusing the multimodal data resulted in a large increase in the recognition rates in comparison with the unimodal systems: the multimodal approach gave an improvement of more than 10% when compared to the most successful unimodal system. Further, the fusion performed at the feature level provided better results than the one performed at the decision level. © 2008 Springer-Verlag Berlin Heidelberg.	en
heal.journalName	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en
dc.identifier.doi	10.1007/978-3-540-85099-1_8	en
dc.identifier.volume	4868 LNCS	en
dc.identifier.spage	92	en
dc.identifier.epage	103	en