Automated facial expressions analysis in schizophrenia: A continuous dynamic approach

Talia Tron*, Abraham Peled, Alexander Grinsphoon, Daphna Weinshall

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

17 Scopus citations

Abstract

Facial expressions play a major role in psychiatric diagnosis, monitoring and treatment adjustment. We recorded 34 schizophrenia patients and matched controls during a clinical interview, and extracted the activity level of 23 facial Action Units (AUs), using 3D structured light cameras and dedicated software. By defining dynamic and intensity AUs activation characteristic features, we found evidence for blunted affect and reduced positive emotional expressions in patients. Further, we designed learning algorithms which achieved up to 85% correct schizophrenia classification rate, and significant correlation with negative symptoms severity. Our results emphasize the clinical importance of facial dynamics, and illustrate the possible advantages of employing affective computing tools in clinical settings.

Original languageAmerican English
Title of host publicationPervasive Computing Paradigms for Mental Health - 5th International Conference, MindCare 2015, Revised Selected Papers
EditorsDimitris Giakoumis, Guillaume Lopez, Aleksandar Matic, Silvia Serino, Pietro Cipresso
PublisherSpringer Verlag
Pages72-81
Number of pages10
ISBN (Print)9783319322698
DOIs
StatePublished - 2016
Event5th International Conference on Pervasive Computing Paradigms for Mental Health, MindCare 2015 - Milan, Italy
Duration: 24 Sep 201525 Sep 2015

Publication series

NameCommunications in Computer and Information Science
Volume604
ISSN (Print)1865-0929

Conference

Conference5th International Conference on Pervasive Computing Paradigms for Mental Health, MindCare 2015
Country/TerritoryItaly
CityMilan
Period24/09/1525/09/15

Bibliographical note

Publisher Copyright:
© Springer International Publishing Switzerland 2016.

Keywords

  • 3D cameras
  • FACS
  • Facial expressions
  • Machine learning
  • Mental health
  • Schizophrenia

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