• Jihun Hamm, Christian G Kohler, Ruben C Gur, and Ragini Verma.
• Department of Radiology, Section of Biomedical Image Analysis, University of Pennsylvania, Philadelphia, PA 19104, USA.
• J. Neurosci. Methods. 2011 Sep 15;200(2):237-56.

AbstractFacial expression is widely used to evaluate emotional impairment in neuropsychiatric disorders. Ekman and Friesen's Facial Action Coding System (FACS) encodes movements of individual facial muscles from distinct momentary changes in facial appearance. Unlike facial expression ratings based on categorization of expressions into prototypical emotions (happiness, sadness, anger, fear, disgust, etc.), FACS can encode ambiguous and subtle expressions, and therefore is potentially more suitable for analyzing the small differences in facial affect. However, FACS rating requires extensive training, and is time consuming and subjective thus prone to bias. To overcome these limitations, we developed an automated FACS based on advanced computer science technology. The system automatically tracks faces in a video, extracts geometric and texture features, and produces temporal profiles of each facial muscle movement. These profiles are quantified to compute frequencies of single and combined Action Units (AUs) in videos, and they can facilitate a statistical study of large populations in disorders known to impact facial expression. We derived quantitative measures of flat and inappropriate facial affect automatically from temporal AU profiles. Applicability of the automated FACS was illustrated in a pilot study, by applying it to data of videos from eight schizophrenia patients and controls. We created temporal AU profiles that provided rich information on the dynamics of facial muscle movements for each subject. The quantitative measures of flatness and inappropriateness showed clear differences between patients and the controls, highlighting their potential in automatic and objective quantification of symptom severity.Copyright © 2011 Elsevier B.V. All rights reserved.

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