Automatic prediction of Depression and Anxiety from behaviour and personality attributes

Current vision based approaches for automatic prediction of mental health conditions like depression and anxiety, rely on models that use behavioural features (usually extracted from faces) only and do not take personality into account. However, there is a considerable amount of evidence that people with certain personality traits are more prone to depression and anxiety disorders. In order to exploit the underlying relationship between personality and these mental health conditions, we propose to use a combination of features consisting of observed facial behaviour and self-reported personality scores. This combination of features is employed for training deep neural networks for predicting depression and anxiety scores. The proposed method was evaluated on a new dataset consisting of personality scores and interview videos from a total of 55 people. The results show that the proposed combination of features significantly improves the prediction performance compared to using the behavioural features alone. This improvement in performance is shown for 2 different kinds of video feature extraction methods and 3 different kinds of interview questionnaires.