SchiNet: Automatic Estimation of Symptoms of Schizophrenia from Facial Behaviour Analysis

TL;DR

This paper introduces SchiNet, a neural network that automatically estimates schizophrenia symptoms from facial behavior in realistic interview videos, using a larger and more natural dataset than prior studies.

Contribution

SchiNet is a novel neural network architecture that analyzes facial expressions to estimate schizophrenia symptoms from realistic patient interviews, expanding dataset size and ecological validity.

Findings

Facial expressions significantly correlate with schizophrenia symptoms.

SchiNet achieves promising accuracy in symptom severity prediction.

The study uses nearly three times more patients than previous research.

Abstract

Patients with schizophrenia often display impairments in the expression of emotion and speech and those are observed in their facial behaviour. Automatic analysis of patients' facial expressions that is aimed at estimating symptoms of schizophrenia has received attention recently. However, the datasets that are typically used for training and evaluating the developed methods, contain only a small number of patients (4-34) and are recorded while the subjects were performing controlled tasks such as listening to life vignettes, or answering emotional questions. In this paper, we use videos of professional-patient interviews, in which symptoms were assessed in a standardised way as they should/may be assessed in practice, and which were recorded in realistic conditions (i.e. varying illumination levels and camera viewpoints) at the patients' homes or at mental health services. We automatically analyse the facial behaviour of 91 out-patients - this is almost 3 times the number of patients in other studies - and propose SchiNet, a novel neural network architecture that estimates expression-related symptoms in two different assessment interviews. We evaluate the proposed SchiNet for patient-independent prediction of symptoms of schizophrenia. Experimental results show that some automatically detected facial expressions are significantly correlated to symptoms of schizophrenia, and that the proposed network for estimating symptom severity delivers promising results.