Folger, Johannes

Kunz, Miriam

Lautenbacher, Stefan

Schmid, Ute

Siebers, Michael

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TALKS 81
Comparing Automated Pain Classifiers with
Human Performance
Johannes Folger1, Michael Siebers1, Miriam Kunz2, Stefan
Lautenbacher2, and Ute Schmid1
1 Cognitive Systems Group, Faculty Information Systems and Applied
Computer Science, University of Bamberg
2 Physiological Psychology, Faculty Human Sciences and Education,
University of Bamberg
Facial expressions play an important role in the communication process.
They usually support the spoken words of communication partners. We
investigated the question whether facial expressions on their own are
sufficient to recognize the feelings of other persons. If this is the case, the
identification of certain emotions could be automatized. We compare
the accuracies of automated classifiers with human rater performance.
Our work is divided in two parts. The first part describes an empirical
study that examines how well subjects estimate the shown emotions of
other people. Subjects (n=60) are required to rate whether the person
shown in an image are in pain, in disgust or in a neutral state.
The second part covers the application of machine learning algorithms
(Support Vector Machines and Decision Trees) to the problem of classi-
fying facial expressions. Only two categories of expressions are con-
sidered, namely pain and disgust, in order to reduce complexity. The
selection of relevant features is based on psychological considerations
and dimensionality reduction mechanisms. We expect that machines
will outperform human raters.
https://doi.org/10.20378/irbo-51771


Comparing Automated Pain Classifiers with Human Performance

Forschungsinformationssystem der Universität Bamberg

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Comparing Automated Pain Classifiers with Human Performance

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