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dc.contributor.authorIrfan, Ben
dc.contributor.authorLyubova, Nen
dc.contributor.authorGarcia Ortiz, Men
dc.contributor.authorBelpaeme, Ten

In this paper, we describe a multi-modal Bayesian network for person recognition in a HRI context, combining information about a person's face, gender, age, and height estimates, with the time of interaction. We conduct an initial study with 14 participants over a four-week period to validate the system and learn the optimal weights for each of the metrics. Several normalisation methods are compared for different settings, such as learning from data, face recognition threshold and quality of the estimation. The results show that the proposed network improves the overall recognition rate by at least 1.4% comparing to person recognition based on face only in an open-set identification problem, and at least 4.4% in a closed-set.

dc.subjectPerson recognitionen
dc.subjectBayesian networken
dc.subjectmulti-modal data fusionen
dc.subjectsoft biometricsen
dc.titleMulti-modal Open-Set Person Identification in HRIen
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering/School of Engineering, Computing and Mathematics
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA/UoA11 Computer Science and Informatics
plymouth.organisational-group/Plymouth/Research Groups
plymouth.organisational-group/Plymouth/Research Groups/Marine Institute
plymouth.organisational-group/Plymouth/Users by role
plymouth.organisational-group/Plymouth/Users by role/Academics
dc.publisher.place2018 ACM/IEEE International Conference on Human-Robot Interaction Social Robots in the Wild workshopen
dc.rights.embargoperiodNot knownen

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