Publication:

Multi-modal Open World User Identification

Date

2022
Journal article
https://doi.org/10.1145/3477963
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dc.contributor.authorIrfan, Bahar
dc.contributor.authorOrtiz, Michael Garcia
dc.contributor.authorLyubova, Natalia
dc.contributor.authorBelpaeme, Tony
dc.contributor.imecauthorBelpaeme, Tony
dc.contributor.orcidimecBelpaeme, Tony::0000-0001-5207-7745
dc.date.accessioned2022-03-10T16:03:45Z
dc.date.available2022-03-10T16:03:45Z
dc.date.issued2022
dc.description.wosFundingTextThis work has been supported by the EU H2020 Marie Sklodowska-Curie Actions Innovative Training Networks project APRIL (grant 674868), Royal Academy of Engineering IAPP project Human-Robot Interaction Strategies for Rehabilitation based on Socially Assistive Robotics (grant IAPP/1516/137), Colciencias (grant 813-2017), the Flemish Government (AI Research Program), the EU H2020 L2TOR project (grant 688014), and the EU FP7 DREAM project (grant 611391).
dc.identifier.doi10.1145/3477963
dc.identifier.issn2573-9522
dc.identifier.urihttps://imec-publications.be/handle/20.500.12860/39407
dc.publisherASSOC COMPUTING MACHINERY
dc.source.beginpageArt.6
dc.source.issue1
dc.source.journalACM TRANSACTIONS ON HUMAN-ROBOT INTERACTION
dc.source.numberofpages50
dc.source.volume11
dc.subject.keywordsWEIGHTED BAYESIAN NETWORK
dc.subject.keywordsFACE
dc.subject.keywordsMODELS
dc.subject.keywordsRECOGNITION
dc.subject.keywordsIMITATION
dc.title

Multi-modal Open World User Identification

dc.typeJournal article
dspace.entity.typePublication
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