Emotion recognition using feature extraction and 3-D models

Karpouzis, K; Votsis, G; Moschovitis, G; Kollias, S

dc.contributor.author	Karpouzis, K	en
dc.contributor.author	Votsis, G	en
dc.contributor.author	Moschovitis, G	en
dc.contributor.author	Kollias, S	en
dc.date.accessioned	2014-03-01T01:48:33Z
dc.date.available	2014-03-01T01:48:33Z
dc.date.issued	1999	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/25517
dc.relation.uri	http://www.scopus.com/inward/record.url?eid=2-s2.0-4944263295&partnerID=40&md5=a39d90f744a1bcec85ba92fe2c8d9907	en
dc.subject	3-d muscle mesh	en
dc.subject	Expression recognition	en
dc.subject	Feature extraction	en
dc.subject	Motion estimation	en
dc.subject.other	Data reduction	en
dc.subject.other	Error analysis	en
dc.subject.other	Extraction	en
dc.subject.other	Image analysis	en
dc.subject.other	Image coding	en
dc.subject.other	Motion estimation	en
dc.subject.other	Neural networks	en
dc.subject.other	Parameter estimation	en
dc.subject.other	Transfer functions	en
dc.subject.other	Vectors	en
dc.subject.other	Visualization	en
dc.subject.other	3-d muscle mesh	en
dc.subject.other	Expression recognition	en
dc.subject.other	Image sequence	en
dc.subject.other	Muscle activation	en
dc.subject.other	Feature extraction	en
dc.title	Emotion recognition using feature extraction and 3-D models	en
heal.type	journalArticle	en
heal.publicationDate	1999	en
heal.abstract	This paper describes an integrated system for human emotion recognition. While other techniques extract explicit motion fields from the areas of interest and combine them with templates or training sets, the proposed system compares evidence of muscle activation from the human face to relevant data taken from a 3-d model of a head. This comparison takes place at curve level, with each curve being drawn from detected feature points in an image sequence or from selected vertices of the polygonal model. The result of this process is identification of the muscles that contribute to the detected motion; this conclusion is then used in conjunction with neural networks that map groups of muscles to emotions. The notion of describing motion with specific points is also supported in MPEG-4 and the relevant encoded data may easily be used in the same context.	en
heal.publisher	World Scientific and Engineering Academy and Society	en
heal.journalName	Computational Intelligence and Applications	en
dc.identifier.spage	360	en
dc.identifier.epage	365	en