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Schizophrenia Research and Treatment
Volume 2014, Article ID 243907, 10 pages
Research Article

Dimensional Information-Theoretic Measurement of Facial Emotion Expressions in Schizophrenia

1Department of Computer Science and Engineering, The Ohio State University, Columbus, OH 43210, USA
2Southern Methodist University, Dallas, TX 75205, USA
3Department of Psychiatry, Neuropsychiatry Section, University of Pennsylvania, Philadelphia, PA 19104, USA
4Philadelphia Veterans Administration Medical Center, Philadelphia, PA 19104, USA
5Department of Radiology, Section of Biomedical Image Analysis, University of Pennsylvania, Philadelphia, PA 19104, USA

Received 9 July 2013; Revised 26 December 2013; Accepted 28 December 2013; Published 25 February 2014

Academic Editor: Robin Emsley

Copyright © 2014 Jihun Hamm et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Altered facial expressions of emotions are characteristic impairments in schizophrenia. Ratings of affect have traditionally been limited to clinical rating scales and facial muscle movement analysis, which require extensive training and have limitations based on methodology and ecological validity. To improve reliable assessment of dynamic facial expression changes, we have developed automated measurements of facial emotion expressions based on information-theoretic measures of expressivity of ambiguity and distinctiveness of facial expressions. These measures were examined in matched groups of persons with schizophrenia ( ) and healthy controls ( ) who underwent video acquisition to assess expressivity of basic emotions (happiness, sadness, anger, fear, and disgust) in evoked conditions. Persons with schizophrenia scored higher on ambiguity, the measure of conditional entropy within the expression of a single emotion, and they scored lower on distinctiveness, the measure of mutual information across expressions of different emotions. The automated measures compared favorably with observer-based ratings. This method can be applied for delineating dynamic emotional expressivity in healthy and clinical populations.