Affiliation:
1. Aix Marseille Université, CNRS, Laboratoire Parole et Langage UMR 7309
Abstract
Abstract
This study presents an automatic tool that allows to trace smile intensities along a video record of
conversational face-to-face interactions. The processed output proposes a sequence of adjusted time intervals labeled following
the Smiling Intensity Scale (Gironzetti, Attardo, and Pickering,
2016), a 5 levels scale varying from neutral facial expression to laughing smile. The underlying statistical model of this
tool is trained on a manually annotated corpus of conversations featuring spontaneous facial expressions. This model will be
detailed in this study. This tool can be used with benefits for annotating smile in interactions. The results are twofold. First,
the evaluation reveals an observed agreement of 68% between manual and automatic annotations. Second, manually correcting the
labels and interval boundaries of the automatic outputs reduces by a factor 10 the annotation time as compared with the time spent
for manually annotating smile intensities without pretreatment. Our annotation engine makes use of the state-of-the-art toolbox
OpenFace for tracking the face and for measuring the intensities of the facial Action Units of interest all along the video. The
documentation and the scripts of our tool, the SMAD software, are available to download at the HMAD open source project URL page
https://github.com/srauzy/HMAD (last access 31 July 2023).
Publisher
John Benjamins Publishing Company
Subject
Linguistics and Language,Experimental and Cognitive Psychology,Communication,Cultural Studies