Computerized measurement of facial expression of emotions in schizophrenia

Christopher Alvino, Christian Kohler, Frederick Barrett, Raquel E. Gur, Ruben C. Gur, Ragini Verma

Research output: Contribution to journalArticlepeer-review

23 Scopus citations


Deficits in the ability to express emotions characterize several neuropsychiatric disorders and are a hallmark of schizophrenia, and there is need for a method of quantifying expression, which is currently done by clinical ratings. This paper presents the development and validation of a computational framework for quantifying emotional expression differences between patients with schizophrenia and healthy controls. Each face is modeled as a combination of elastic regions, and expression changes are modeled as a deformation between a neutral face and an expressive face. Functions of these deformations, known as the regional volumetric difference (RVD) functions, form distinctive quantitative profiles of expressions. Employing pattern classification techniques, we have designed expression classifiers for the four universal emotions of happiness, sadness, anger and fear by training on RVD functions of expression changes. The classifiers were cross-validated and then applied to facial expression images of patients with schizophrenia and healthy controls. The classification score for each image reflects the extent to which the expressed emotion matches the intended emotion. Group-wise statistical analysis revealed this score to be significantly different between healthy controls and patients, especially in the case of anger. This score correlated with clinical severity of flat affect. These results encourage the use of such deformation based expression quantification measures for research in clinical applications that require the automated measurement of facial affect.

Original languageEnglish (US)
Pages (from-to)350-361
Number of pages12
JournalJournal of Neuroscience Methods
Issue number2
StatePublished - Jul 30 2007
Externally publishedYes


  • Elastic deformations
  • Facial expressions quantification
  • Pattern classification
  • Regional volumetric differences
  • Support vector machines

ASJC Scopus subject areas

  • Neuroscience(all)


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