Automated facial expressions analysis in schizophrenia: A continuous dynamic approach

Talia Tron; Abraham Peled; Alexander Grinsphoon; Daphna Weinshall

doi:10.1007/978-3-319-32270-4_8

Automated facial expressions analysis in schizophrenia: A continuous dynamic approach

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

^*Corresponding author for this work

The Rachel and Selim Benin School of Engineering and Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

22 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 language	English
Title of host publication	Pervasive Computing Paradigms for Mental Health - 5th International Conference, MindCare 2015, Revised Selected Papers
Editors	Dimitris Giakoumis, Guillaume Lopez, Aleksandar Matic, Silvia Serino, Pietro Cipresso
Publisher	Springer Verlag
Pages	72-81
Number of pages	10
ISBN (Print)	9783319322698
DOIs	https://doi.org/10.1007/978-3-319-32270-4_8
State	Published - 2016
Event	5th International Conference on Pervasive Computing Paradigms for Mental Health, MindCare 2015 - Milan, Italy Duration: 24 Sep 2015 → 25 Sep 2015

Publication series

Name	Communications in Computer and Information Science
Volume	604
ISSN (Print)	1865-0929

Conference

Conference	5th International Conference on Pervasive Computing Paradigms for Mental Health, MindCare 2015
Country/Territory	Italy
City	Milan
Period	24/09/15 → 25/09/15

Bibliographical note

Publisher Copyright:
© Springer International Publishing Switzerland 2016.

Keywords

3D cameras
FACS
Facial expressions
Machine learning
Mental health
Schizophrenia

Access to Document

10.1007/978-3-319-32270-4_8

Cite this

Tron, T., Peled, A., Grinsphoon, A., & Weinshall, D. (2016). Automated facial expressions analysis in schizophrenia: A continuous dynamic approach. In D. Giakoumis, G. Lopez, A. Matic, S. Serino, & P. Cipresso (Eds.), Pervasive Computing Paradigms for Mental Health - 5th International Conference, MindCare 2015, Revised Selected Papers (pp. 72-81). (Communications in Computer and Information Science; Vol. 604). Springer Verlag. https://doi.org/10.1007/978-3-319-32270-4_8

Tron, Talia ; Peled, Abraham ; Grinsphoon, Alexander et al. / Automated facial expressions analysis in schizophrenia : A continuous dynamic approach. Pervasive Computing Paradigms for Mental Health - 5th International Conference, MindCare 2015, Revised Selected Papers. editor / Dimitris Giakoumis ; Guillaume Lopez ; Aleksandar Matic ; Silvia Serino ; Pietro Cipresso. Springer Verlag, 2016. pp. 72-81 (Communications in Computer and Information Science).

@inproceedings{2c71764bf8944a8cb2d553ed4d41f61c,

title = "Automated facial expressions analysis in schizophrenia: A continuous dynamic approach",

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.",

keywords = "3D cameras, FACS, Facial expressions, Machine learning, Mental health, Schizophrenia",

author = "Talia Tron and Abraham Peled and Alexander Grinsphoon and Daphna Weinshall",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2016.; 5th International Conference on Pervasive Computing Paradigms for Mental Health, MindCare 2015 ; Conference date: 24-09-2015 Through 25-09-2015",

year = "2016",

doi = "10.1007/978-3-319-32270-4_8",

language = "אנגלית",

isbn = "9783319322698",

series = "Communications in Computer and Information Science",

publisher = "Springer Verlag",

pages = "72--81",

editor = "Dimitris Giakoumis and Guillaume Lopez and Aleksandar Matic and Silvia Serino and Pietro Cipresso",

booktitle = "Pervasive Computing Paradigms for Mental Health - 5th International Conference, MindCare 2015, Revised Selected Papers",

address = "גרמניה",

}

Tron, T, Peled, A, Grinsphoon, A & Weinshall, D 2016, Automated facial expressions analysis in schizophrenia: A continuous dynamic approach. in D Giakoumis, G Lopez, A Matic, S Serino & P Cipresso (eds), Pervasive Computing Paradigms for Mental Health - 5th International Conference, MindCare 2015, Revised Selected Papers. Communications in Computer and Information Science, vol. 604, Springer Verlag, pp. 72-81, 5th International Conference on Pervasive Computing Paradigms for Mental Health, MindCare 2015, Milan, Italy, 24/09/15. https://doi.org/10.1007/978-3-319-32270-4_8

Automated facial expressions analysis in schizophrenia: A continuous dynamic approach. / Tron, Talia; Peled, Abraham; Grinsphoon, Alexander et al.
Pervasive Computing Paradigms for Mental Health - 5th International Conference, MindCare 2015, Revised Selected Papers. ed. / Dimitris Giakoumis; Guillaume Lopez; Aleksandar Matic; Silvia Serino; Pietro Cipresso. Springer Verlag, 2016. p. 72-81 (Communications in Computer and Information Science; Vol. 604).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Automated facial expressions analysis in schizophrenia

T2 - 5th International Conference on Pervasive Computing Paradigms for Mental Health, MindCare 2015

AU - Tron, Talia

AU - Peled, Abraham

AU - Grinsphoon, Alexander

AU - Weinshall, Daphna

N1 - Publisher Copyright: © Springer International Publishing Switzerland 2016.

PY - 2016

Y1 - 2016

N2 - 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.

AB - 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.

KW - 3D cameras

KW - FACS

KW - Facial expressions

KW - Machine learning

KW - Mental health

KW - Schizophrenia

UR - http://www.scopus.com/inward/record.url?scp=84964056642&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-32270-4_8

DO - 10.1007/978-3-319-32270-4_8

M3 - ???researchoutput.researchoutputtypes.contributiontobookanthology.conference???

AN - SCOPUS:84964056642

SN - 9783319322698

T3 - Communications in Computer and Information Science

SP - 72

EP - 81

BT - Pervasive Computing Paradigms for Mental Health - 5th International Conference, MindCare 2015, Revised Selected Papers

A2 - Giakoumis, Dimitris

A2 - Lopez, Guillaume

A2 - Matic, Aleksandar

A2 - Serino, Silvia

A2 - Cipresso, Pietro

PB - Springer Verlag

Y2 - 24 September 2015 through 25 September 2015

ER -

Tron T, Peled A, Grinsphoon A, Weinshall D. Automated facial expressions analysis in schizophrenia: A continuous dynamic approach. In Giakoumis D, Lopez G, Matic A, Serino S, Cipresso P, editors, Pervasive Computing Paradigms for Mental Health - 5th International Conference, MindCare 2015, Revised Selected Papers. Springer Verlag. 2016. p. 72-81. (Communications in Computer and Information Science). doi: 10.1007/978-3-319-32270-4_8

Automated facial expressions analysis in schizophrenia: A continuous dynamic approach

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

Other files and links

Fingerprint

Cite this