The classification score for each image reflects the extent to which the expressed emotion matches the intended emotion.

The classifiers were cross-validated and then applied to facial expression images of patients with schizophrenia and healthy controls. 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. Functions of these deformations, known as the regional volumetric difference (RVD) functions, form distinctive quantitative profiles of expressions. 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.

This paper presents the development and validation of a computational framework for quantifying emotional expression differences between patients with schizophrenia and healthy controls. 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.

Flat affect face