loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Andreas Risskov 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

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.149.239.79

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

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 (VISIGRAPP 2021) - HUCAPP; ISBN 978-989-758-488-6; ISSN 2184-4321, SciTePress, pages 198-204. DOI: 10.5220/0010259301980204

@conference{hucapp21,
author={Andreas Risskov 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 (VISIGRAPP 2021) - 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 (VISIGRAPP 2021) - 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
PB - SciTePress

- Science and Technology Publications, Lda.
RESOURCES

Proceedings

Papers

Authors

Ontology

CONTACTS

Science and Technology Publications, Lda
Avenida de S. Francisco Xavier, Lote 7 Cv. C,
2900-616 Setúbal, Portugal.

Phone: +351 265 520 185 (National fixed network call)
Fax: +351 265 520 186
Email: info@scitepress.org

EXTERNAL LINKS

PRIMORIS

INSTICC

SCITEVENTS

CROSSREF

PROCEEDINGS SUBMITTED FOR INDEXATION BY:

dblp

Ei Compendex

SCOPUS

Semantic Scholar

Google Scholar

Microsoft Academic