Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/10467
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dc.contributor.authorMeng, H-
dc.contributor.authorBianchi-Berthouze, N-
dc.contributor.authorDeng, Y-
dc.contributor.authorCheng, J-
dc.contributor.authorCosmas, J-
dc.date.accessioned2015-03-23T11:17:11Z-
dc.date.available2015-03-23T11:17:11Z-
dc.date.issued2016-
dc.identifier.citationIEEE Transactions on Cybernetics, 46(4): pp. 916-929, (2016)en_US
dc.identifier.issn2168-2267-
dc.identifier.urihttp://www.ieeesmc.org/publications/transactions-on-cybernetics-
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/10467-
dc.description"(c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works."en_US
dc.description.abstractAutomatic continuous affective state prediction from naturalistic facial expression is a very challenging research topic but very important in human-computer interaction. One of the main challenges is modeling the dynamics that characterize naturalistic expressions. In this paper, a novel two-stage automatic system is proposed to continuously predict affective dimension values from facial expression videos. In the first stage, traditional regression methods are used to classify each individual video frame, while in the second stage, a Time-Delay Neural Network (TDNN) is proposed to model the temporal relationships between consecutive predictions. The two-stage approach separates the emotional state dynamics modeling from an individual emotional state prediction step based on input features. In doing so, the temporal information used by the TDNN is not biased by the high variability between features of consecutive frames and allows the network to more easily exploit the slow changing dynamics between emotional states. The system was fully tested and evaluated on three different facial expression video datasets. Our experimental results demonstrate that the use of a two-stage approach combined with the TDNN to take into account previously classified frames significantly improves the overall performance of continuous emotional state estimation in naturalistic facial expressions. The proposed approach has won the affect recognition sub-challenge of the third international Audio/Visual Emotion Recognition Challenge (AVEC2013)1.en_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.subjectAffective computingen_US
dc.subjectNeural networksen_US
dc.subjectEmotion predictionen_US
dc.subjectEmotion dimensionen_US
dc.subjectFacial expressionen_US
dc.titleTime-delay neural network for continuous emotional dimension prediction from facial expression sequencesen_US
dc.typeArticleen_US
dc.identifier.doihttp://dx.doi.org/10.1109/TCYB.2015.2418092-
dc.relation.isPartOfIEEE Transactions on Cybernetics-
dc.relation.isPartOfIEEE Transactions on Cybernetics-
pubs.publication-statusAccepted-
pubs.publication-statusAccepted-
pubs.organisational-data/Brunel-
pubs.organisational-data/Brunel/Brunel Staff by College/Department/Division-
pubs.organisational-data/Brunel/Brunel Staff by College/Department/Division/College of Engineering, Design and Physical Sciences-
pubs.organisational-data/Brunel/Brunel Staff by College/Department/Division/College of Engineering, Design and Physical Sciences/Dept of Electronic and Computer Engineering-
pubs.organisational-data/Brunel/Brunel Staff by College/Department/Division/College of Engineering, Design and Physical Sciences/Dept of Electronic and Computer Engineering/Electronic and Computer Engineering-
pubs.organisational-data/Brunel/Brunel Staff by Institute/Theme-
pubs.organisational-data/Brunel/Brunel Staff by Institute/Theme/Institute of Environmental, Health and Societies-
pubs.organisational-data/Brunel/Brunel Staff by Institute/Theme/Institute of Environmental, Health and Societies/Biomedical Engineering and Healthcare Technologies-
Appears in Collections:Dept of Electronic and Computer Engineering Research Papers

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