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Authors: Andreas Sørensen ; Oskar Palinko and Norbert Krüger

Affiliation: The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Campusvej 55, DK-5230 Odense M, Denmark

Keyword(s): Human-robot Interaction, Visual Interest, Gaze, Classification.

Abstract: It is important for a social robot to know if a nearby human is showing interest in interacting with it. We approximate this interest with expressed visual interest. To find it, we train a number of classifiers with previously labeled data. The input features for these are facial features like head orientation, eye gaze and facial action units, which are provided by the OpenFace library. As training data, we use video footage collected during an in-the-wild human-robot interaction scenario, where a social robot was approaching people at a cafeteria to serve them water. The most successful classifier that we trained tested at a 94% accuracy for detecting interest on an unrelated testing dataset. This allows us to create an effective tool for our social robot, which enables it to start talking to people only when it is fairly certain that the addressed persons are interested in talking to it.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Sørensen, A.; Palinko, O. and Krüger, N. (2021). Classification of Visual Interest based on Gaze and Facial Features for Human-robot Interaction. In Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - HUCAPP, ISBN 978-989-758-488-6; ISSN 2184-4321, pages 198-204. DOI: 10.5220/0010259301980204

@conference{hucapp21,
author={Andreas Sørensen. and Oskar Palinko. and Norbert Krüger.},
title={Classification of Visual Interest based on Gaze and Facial Features for Human-robot Interaction},
booktitle={Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - HUCAPP,},
year={2021},
pages={198-204},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010259301980204},
isbn={978-989-758-488-6},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - HUCAPP,
TI - Classification of Visual Interest based on Gaze and Facial Features for Human-robot Interaction
SN - 978-989-758-488-6
IS - 2184-4321
AU - Sørensen, A.
AU - Palinko, O.
AU - Krüger, N.
PY - 2021
SP - 198
EP - 204
DO - 10.5220/0010259301980204