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dc.contributor.authorYu, Xiang
dc.contributor.authorZhang, Shaoting
dc.contributor.authorYu, Yang
dc.contributor.authorDunbar, Norah
dc.contributor.authorJensen, Matthew
dc.contributor.authorBurgoon, Judee K.
dc.contributor.authorMetaxas, Dimitris N.
dc.date.accessioned2021-01-26T20:52:00Z
dc.date.available2021-01-26T20:52:00Z
dc.date.issued2013-04
dc.identifier.citationX. Yu et al., "Automated analysis of interactional synchrony using robust facial tracking and expression recognition," 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), Shanghai, 2013, pp. 1-6, doi: 10.1109/FG.2013.6553802.en_US
dc.identifier.doi10.1109/fg.2013.6553802
dc.identifier.urihttp://hdl.handle.net/10150/651071
dc.description.abstractIn this paper, we propose an automated, data-driven and unobtrusive framework to analyze interactional synchrony. We use this information to determine whether interpersonal synchrony can be an indicator of deceit. Our framework includes a robust facial tracking module, an effective expression recognition method, synchrony feature extraction and feature selection methods. These synchrony features are used to learn classification models for the deception recognition. To evaluate our proposed framework, we have conducted extensive experiments on a database of 242 video samples. We validate the performance of each technical module in our framework, and also show that these synchrony features are very effective at detecting deception.en_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.rightsCopyright © 2013, IEEE.en_US
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en_US
dc.source2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG)
dc.subjectFaceen_US
dc.subjectShapeen_US
dc.subjectVectorsen_US
dc.subjectFeature extractionen_US
dc.subjectAccuracyen_US
dc.subjectCorrelationen_US
dc.subjectVisualizationen_US
dc.titleAutomated analysis of interactional synchrony using robust facial tracking and expression recognitionen_US
dc.typeProceedingsen_US
dc.identifier.journal2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG)en_US
dc.description.collectioninformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.en_US
dc.eprint.versionFinal accepted manuscripten_US
dc.identifier.eisbn9781467355469
refterms.dateFOA2021-01-26T20:52:00Z


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