Intelligent facial analysis and expression recognition

Ioannou, S; Wallace, M; Kollias, S

dc.contributor.author	Ioannou, S	en
dc.contributor.author	Wallace, M	en
dc.contributor.author	Kollias, S	en
dc.date.accessioned	2014-03-01T02:44:04Z
dc.date.available	2014-03-01T02:44:04Z
dc.date.issued	2006	en
dc.identifier.issn	10987576	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/31656
dc.subject	Boundary Detection	en
dc.subject	Error Resilience	en
dc.subject	Expression Analysis	en
dc.subject	Facial Expression	en
dc.subject	Facial Features	en
dc.subject	Feature Extraction	en
dc.subject	Interactive System	en
dc.subject	Multiple Channels	en
dc.subject	Social Psychology	en
dc.subject	Neural Network	en
dc.subject.other	Boundary conditions	en
dc.subject.other	Error analysis	en
dc.subject.other	Feature extraction	en
dc.subject.other	Human computer interaction	en
dc.subject.other	Intelligent systems	en
dc.subject.other	Expression recognition	en
dc.subject.other	Facial feature boundary detection	en
dc.subject.other	Human communication	en
dc.subject.other	Intelligent facial analysis	en
dc.subject.other	Gesture recognition	en
dc.title	Intelligent facial analysis and expression recognition	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/IJCNN.2006.246926	en
heal.identifier.secondary	http://dx.doi.org/10.1109/IJCNN.2006.246926	en
heal.identifier.secondary	1716654	en
heal.publicationDate	2006	en
heal.abstract	Since facial expressions are a key modality in human communication, the automated analysis of facial images and video for the estimation of the displayed expression is central in the design of intuitive and human friendly computer interaction systems. In this paper we present an intelligent feature extraction system which combines analysis from multiple channels based on their confidence, to result in better, error resilient facial feature boundary detection. Neural networks are a key component of the system. Issues such as uncertainty and lack of confidence in the process of feature extraction are considered during the expression analysis and recognition. Various results are presented which illustrate the performance of the method. © 2006 IEEE.	en
heal.journalName	IEEE International Conference on Neural Networks - Conference Proceedings	en
dc.identifier.doi	10.1109/IJCNN.2006.246926	en
dc.identifier.spage	4029	en
dc.identifier.epage	4036	en