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dc.contributor.authorCeliktutan, Oen
dc.contributor.authorSkordos, Sen
dc.contributor.authorGunes, Haticeen
dc.date.accessioned2017-10-02T11:10:05Z
dc.date.available2017-10-02T11:10:05Z
dc.identifier.issn1949-3045
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/267481
dc.description.abstractIn this paper we introduce a novel dataset, the Multimodal Human-Human-Robot-Interactions (MHHRI) dataset, with the aim of studying personality simultaneously in human-human interactions (HHI) and human-robot interactions (HRI) and its relationship with engagement. Multimodal data was collected during a controlled interaction study where dyadic interactions between two human participants and triadic interactions between two human participants and a robot took place with interactants asking a set of personal questions to each other. Interactions were recorded using two static and two dynamic cameras as well as two biosensors, and meta-data was collected by having participants fill in two types of questionnaires, for assessing their own personality traits and their perceived engagement with their partners (self labels) and for assessing personality traits of the other participants partaking in the study (acquaintance labels). As a proof of concept, we present baseline results for personality and engagement classification. Our results show that (i) trends in personality classification performance remain the same with respect to the self and the acquaintance labels across the HHI and HRI settings; (ii) for extroversion, the acquaintance labels yield better results as compared to the self labels; (iii) in general, multi-modality yields better performance for the classification of personality traits.
dc.description.sponsorshipThis work was funded by the EPSRC under its IDEAS Factory Sandpits call on Digital Personhood (Grant Ref: EP/L00416X/1).
dc.language.isoenen
dc.publisherIEEE
dc.titleMultimodal Human-Human-Robot Interactions (MHHRI) Dataset for Studying Personality and Engagementen
dc.typeArticle
prism.issueIdentifier99en
prism.publicationNameIEEE Transactions on Affective Computingen
prism.volumePPen
dc.identifier.doi10.17863/CAM.13433
dcterms.dateAccepted2017-07-26en
rioxxterms.versionofrecord10.1109/TAFFC.2017.2737019en
rioxxterms.versionAMen
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2017-07-26en
dc.contributor.orcidGunes, Hatice [0000-0003-2407-3012]
dc.identifier.eissn1949-3045
rioxxterms.typeJournal Article/Reviewen
pubs.funder-project-idEPSRC (via University of Exeter) (EP/L00416X/1)
cam.issuedOnline2017-08-09en


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