Classification of visual interest based on gaze and facial features for human-robot interaction

Andreas Risskov Sørensen, Oskar Palinko*, Norbert Krüger

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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.

Original languageEnglish
Title of host publicationProceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - HUCAPP
EditorsAlexis Paljic, Tabitha Peck, Jose Braz, Kadi Bouatouch
Volume2
PublisherSCITEPRESS Digital Library
Publication date2021
Pages198-204
ISBN (Electronic)9789897584886
DOIs
Publication statusPublished - 2021
Event16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2021 - Virtual, Online
Duration: 8. Feb 202110. Feb 2021

Conference

Conference16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2021
CityVirtual, Online
Period08/02/202110/02/2021
SponsorInstitute for Systems and Technologies of Information, Control and Communication (INSTICC)
SeriesIVAPP
Volume2
ISSN2184-4321

Keywords

  • Classification
  • Gaze
  • Human-robot interaction
  • Visual interest

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