Adaptive on-line neural network retraining for real life multimodal emotion recognition

Ioannou, S; Kessous, L; Caridakis, G; Karpouzis, K; Aharonson, V; Kollias, S

dc.contributor.author	Ioannou, S	en
dc.contributor.author	Kessous, L	en
dc.contributor.author	Caridakis, G	en
dc.contributor.author	Karpouzis, K	en
dc.contributor.author	Aharonson, V	en
dc.contributor.author	Kollias, S	en
dc.date.accessioned	2014-03-01T02:43:53Z
dc.date.available	2014-03-01T02:43:53Z
dc.date.issued	2006	en
dc.identifier.issn	0302-9743	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/31540
dc.subject	Adaptive Neural Network	en
dc.subject	Emotion Recognition	en
dc.subject	Facial Expression	en
dc.subject	Neural Network Classifier	en
dc.subject	Neural Network	en
dc.subject.classification	Computer Science, Theory & Methods	en
dc.subject.other	Emotion recognition	en
dc.subject.other	Facial expression	en
dc.subject.other	Prosodic audio features	en
dc.subject.other	Unimodal speech	en
dc.subject.other	Database systems	en
dc.subject.other	Human computer interaction	en
dc.subject.other	Online systems	en
dc.subject.other	Real time systems	en
dc.subject.other	Speech recognition	en
dc.subject.other	Neural networks	en
dc.title	Adaptive on-line neural network retraining for real life multimodal emotion recognition	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1007/11840817_9	en
heal.identifier.secondary	http://dx.doi.org/10.1007/11840817_9	en
heal.language	English	en
heal.publicationDate	2006	en
heal.abstract	Emotions play a major role in human-to-human communication enabling people to express themselves beyond the verbal domain. In recent years, important advances have been made in unimodal speech and video emotion analysis where facial expression information and prosodic audio features are treated independently. The need however to combine the two modalities in a naturalistic context, where adaptation to specific human characteristics and expressivity is required, and where single modalities alone cannot provide satisfactory evidence, is clear. Appropriate neural network classifiers are proposed for multimodal emotion analysis in this paper, in an adaptive framework, which is able to activate retraining of each modality, whenever deterioration of the respective performance is detected. Results are presented based on the IST HUMAINE NoE naturalistic database; both facial expression information and prosodic audio features are extracted from the same data and feature-based emotion analysis is performed through the proposed adaptive neural network methodology. © Springer-Verlag Berlin Heidelberg 2006.	en
heal.publisher	SPRINGER-VERLAG BERLIN	en
heal.journalName	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en
heal.bookName	LECTURE NOTES IN COMPUTER SCIENCE	en
dc.identifier.doi	10.1007/11840817_9	en
dc.identifier.isi	ISI:000241472100009	en
dc.identifier.volume	4131 LNCS - I	en
dc.identifier.spage	81	en
dc.identifier.epage	92	en