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Using automated computer vision and machine learning to code facial expressions of affect and arousal: Implications for emotion dysregulation research

Published online by Cambridge University Press:  28 March 2019

Nathaniel Haines
Affiliation:
Department of Psychology, Ohio State University, Columbus, OH, USA
Ziv Bell
Affiliation:
Department of Psychology, Ohio State University, Columbus, OH, USA
Sheila Crowell
Affiliation:
Department of Psychology, University of Utah, Salt Lake City, UT, USA Department of Psychiatry, University of Utah, Salt Lake City, UT, USA
Hunter Hahn
Affiliation:
Department of Psychology, Ohio State University, Columbus, OH, USA
Dana Kamara
Affiliation:
Department of Psychology, Ohio State University, Columbus, OH, USA
Heather McDonough-Caplan
Affiliation:
Department of Psychology, Ohio State University, Columbus, OH, USA
Tiffany Shader
Affiliation:
Department of Psychology, Ohio State University, Columbus, OH, USA
Theodore P. Beauchaine*
Affiliation:
Department of Psychology, Ohio State University, Columbus, OH, USA
*
Author for Correspondence: Theodore P. Beauchaine, Department of Psychology, Ohio State University, 1835 Neil Avenue, Columbus, OH 43210; Email: beauchaine.1@osu.edu.

Abstract

As early as infancy, caregivers’ facial expressions shape children's behaviors, help them regulate their emotions, and encourage or dissuade their interpersonal agency. In childhood and adolescence, proficiencies in producing and decoding facial expressions promote social competence, whereas deficiencies characterize several forms of psychopathology. To date, however, studying facial expressions has been hampered by the labor-intensive, time-consuming nature of human coding. We describe a partial solution: automated facial expression coding (AFEC), which combines computer vision and machine learning to code facial expressions in real time. Although AFEC cannot capture the full complexity of human emotion, it codes positive affect, negative affect, and arousal—core Research Domain Criteria constructs—as accurately as humans, and it characterizes emotion dysregulation with greater specificity than other objective measures such as autonomic responding. We provide an example in which we use AFEC to evaluate emotion dynamics in mother–daughter dyads engaged in conflict. Among other findings, AFEC (a) shows convergent validity with a validated human coding scheme, (b) distinguishes among risk groups, and (c) detects developmental increases in positive dyadic affect correspondence as teen daughters age. Although more research is needed to realize the full potential of AFEC, findings demonstrate its current utility in research on emotion dysregulation.

Type
Special Issue Articles
Copyright
Copyright © Cambridge University Press 2019 

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