Machine Learning Facial Emotion Classifiers in Psychotherapy Research: A Proof-of-Concept Study.

Journal: Psychopathology
PMID: 38011846

Abstract

BACKGROUND: New advances in the field of machine learning make it possible to track facial emotional expression with high resolution, including micro-expressions. These advances have promising applications for psychotherapy research, since manual coding (e.g., the Facial Action Coding System), is time-consuming.

Authors

  • Martin Steppan
    Faculty of Psychology, University of Basel, Basel, Switzerland.
  • Ronan Zimmermann
    Faculty of Psychology, University of Basel, Basel, Switzerland.
  • Lukas Fürer
    Psychiatric University Hospital, Basel, Switzerland.
  • Matthew Southward
    Department of Psychology, University of Kentucky, Lexington, Kentucky, USA.
  • Julian Koenig
    University Hospital of Child and Adolescent Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland.
  • Michael Kaess
    University Hospital of Child and Adolescent Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland.
  • Johann Roland Kleinbub
    Department of Philosophy, Sociology, Education and Applied Psychology, University of Padova, 35139, Padua, Italy.
  • Volker Roth
    Department of Mathematics and Informatics, University of Basel, Basel, Switzerland.
  • Klaus Schmeck
    Psychiatric University Hospital, Basel, Switzerland.

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