The Communicative Face
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2019-12-01
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en
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Abstract
One of the most fundamental human activities consists of communication through
human language. The most important aspect of human language is face-to-face
interaction, suggesting human language is a multimodal phenomenon. There is
an enormous variation in the face's articulators and the potential signals they
can produce. This research will investigate whether any regularities regarding
those facial signals occur when comparing questions to responses. Data from
dyadic conversations were used where participants talked freely for 60 minutes,
in an attempt to take a more naturalistic approach compared to most existing
literature that uses conversational data from highly controlled environments.
Facial signals are recognized with the help of OpenFace, a tool for the automatic
detection of facial signals from video data. General (co-)occurrence
counts of facial signals, as well as sequences of facial signals, and their timing
were analyzed while comparing questions to responses. Signi cant di erences
between questions and responses were found both agreeing, as well contradicting
existing literature. Therefore this research could provide more insight to what
facial signals occur systematically during questions and responses and possibly
help to addressee to predict the content or ending of the incoming turn.
Furthermore, SPeeding Up the Detection of Non-iconic and Iconic Gestures
(SPUDNIG): a toolkit for the automatic detection of hand movements and gestures
in video data was presented. This toolkit was developed since there was
no existing toolkit for the automatic detection of hand movements, in contrast
to toolkits for the automatic detection of facial signals such as OpenFace. It
was demonstrated that SPUDNIG accelerates the process of annotating hand
gestures. Therefore SPUDNIG could be used in order to facilitate the timeconsuming
and labour-intensive task of manually annotating hand gestures.
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Faculteit der Sociale Wetenschappen