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dc.contributor.authorChaturvedi, Iti
dc.contributor.authorPandelea, Vlad
dc.contributor.authorCambria, Erik
dc.contributor.authorWelsch, Roy
dc.contributor.authorDatta, Bithin
dc.date.accessioned2024-08-26T18:25:31Z
dc.date.available2024-08-26T18:25:31Z
dc.date.issued2024-08-24
dc.identifier.urihttps://hdl.handle.net/1721.1/156393
dc.description.abstractIn this paper, we target the problem of generating facial expressions from a piece of audio. This is challenging since both audio and video have inherent characteristics that are distinct from the other. Some words may have identical lip movements, and speech impediments may prevent lip-reading in some individuals. Previous approaches to generating such a talking head suffered from stiff expressions. This is because they focused only on lip movements and the facial landmarks did not contain the information flow from the audio. Hence, in this work, we employ spatio-temporal independent component analysis to accurately sync the audio with the corresponding face video. Proper word formation also requires control over the face muscles that can be captured using a barrier function. We first validated the approach on the diffusion of salt water in coastal areas using a synthetic finite element simulation. Next, we applied it to 3D facial expressions in toddlers for which training data is difficult to capture. Prior knowledge in the form of rules is specified using Fuzzy logic, and multi-objective optimization is used to collectively learn a set of rules. We observed significantly higher F-measure on three real-world problems.en_US
dc.publisherSpringer USen_US
dc.relation.isversionofhttps://doi.org/10.1007/s12559-024-10344-7en_US
dc.rightsCreative Commons Attributionen_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.sourceSpringer USen_US
dc.titleBarrier Function to Skin Elasticity in Talking Headen_US
dc.typeArticleen_US
dc.identifier.citationChaturvedi, I., Pandelea, V., Cambria, E. et al. Barrier Function to Skin Elasticity in Talking Head. Cogn Comput (2024).en_US
dc.contributor.departmentSloan School of Management
dc.relation.journalCognitive Computationen_US
dc.identifier.mitlicensePUBLISHER_CC
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2024-08-25T03:18:42Z
dc.language.rfc3066en
dc.rights.holderThe Author(s)
dspace.embargo.termsN
dspace.date.submission2024-08-25T03:18:42Z
mit.licensePUBLISHER_CC
mit.metadata.statusAuthority Work and Publication Information Neededen_US


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