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Adaptation of facial feature extraction and rule generation in emotion-analysis systems

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dc.contributor.author Ioannou, S en
dc.contributor.author Raouzaiou, A en
dc.contributor.author Karpouzis, K en
dc.contributor.author Kollias, S en
dc.date.accessioned 2014-03-01T02:42:26Z
dc.date.available 2014-03-01T02:42:26Z
dc.date.issued 2004 en
dc.identifier.issn 10987576 en
dc.identifier.uri http://hdl.handle.net/123456789/31005
dc.subject Emotion Recognition en
dc.subject Environmental Conditions en
dc.subject Facial Expression en
dc.subject Facial Feature Extraction en
dc.subject Facial Features en
dc.subject Human Computer Interaction en
dc.subject neuro-fuzzy system en
dc.subject Rule Extraction en
dc.subject.other Animation en
dc.subject.other Database systems en
dc.subject.other Human computer interaction en
dc.subject.other Linguistics en
dc.subject.other Motion pictures en
dc.subject.other Optimization en
dc.subject.other Project management en
dc.subject.other Psychophysiology en
dc.subject.other Synchronization en
dc.subject.other Face and Body Animation (FBA) en
dc.subject.other Facial Animation Parameters (FAP) en
dc.subject.other Facial Definition Parameters (FDP) en
dc.subject.other Feature points (FP) en
dc.subject.other Feature extraction en
dc.title Adaptation of facial feature extraction and rule generation in emotion-analysis systems en
heal.type conferenceItem en
heal.identifier.primary 10.1109/IJCNN.2004.1379962 en
heal.identifier.secondary http://dx.doi.org/10.1109/IJCNN.2004.1379962 en
heal.publicationDate 2004 en
heal.abstract The paper addresses the problem of emotion recognition in faces through an intelligent neuro-fuzzy system, where the extraction of facial features follows the MPEG-4 standard and is adapted to particular environmental conditions and specific persons. These features are associated to symbolic fuzzy predicates providing the classification of facial images according to the underlying emotional states. For this classification we use rules extracted from psychological studies and expression databases including extreme expressions such as those illustrated in Ekman's database. The rules are then refined in realistic conditions, taking into account the extracted features. The experimental results, based both in extreme and naturalistic databases developed in the frameworks of IST ERMIS and NoE HUMAINE, illustrate the capability of the developed system to analyse and recognise facial expressions in human computer interaction applications. en
heal.journalName IEEE International Conference on Neural Networks - Conference Proceedings en
dc.identifier.doi 10.1109/IJCNN.2004.1379962 en
dc.identifier.volume 1 en
dc.identifier.spage 513 en
dc.identifier.epage 518 en


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