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 [BibTeX] [Marc21]
Supervised Gaze Bias Correction for Gaze Coding in Interactions
Type of publication: Idiap-RR
Citation: Siegfried_Idiap-RR-23-2017
Number: Idiap-RR-23-2017
Year: 2017
Month: 9
Institution: Idiap
Abstract: Understanding the role of gaze in conversations and social interactions or exploiting it for HRI applications is an ongoing research subject. In these contexts, vision based eye trackers are preferred as they are non-invasive and allow people to behave more naturally. In particular, appearance based methods (ABM) are very promising, as they can perform online gaze estima- tion and have the potential to be head pose and person invariant, accommodate more situations as well as user mobility and the resulting low resolution images. However, they may also suffer from a lack of robustness when several of these challenges are jointly present. In this work, we address gaze coding in human-human interactions, and present a simple method based on a few manually annotated frames that is able to much reduce the error of a head pose invariant ABM method, as shown on a dataset of 6 interactions.
Keywords: appearance model, attention, bias correction, eye tracking, Gaze, usability
Projects Idiap
Authors Siegfried, Remy
Odobez, Jean-Marc
Crossref by Siegfried_ECEM_2017
Added by: [ADM]
Total mark: 0
  • Siegfried_Idiap-RR-23-2017.pdf (MD5: 78d21da793b8055db71b009391a65ca7)