Recent years have seen a lot of activity in affective computing for automated analysis of depression. However, no research has so far directly evaluated the performance of facial behavioural analysis methods in classifying different subtypes of depression such as melancholia. The mental state assessment of a mood disorder depends largely on appearance, behaviour, speech, thought, perception, mood and facial affect. Mood and facial affect mainly contribute to distinguishing melancholia from non-melancholia. These are assessed by clinicians, and hence vulnerable to subjective judgement. As a result, clinical assessment alone may not accurately capture the presence or absence of specific disorders such as melancholia, a distressing condition whose presence has important treatment implications. Melancholia is characterised by severe anhedonia and psychomotor disturbance, which can be a mix of motor retardation with periods of superimposed agitation. To the best of our knowledge, this study is the first attempt to perform facial behavioural analysis to disambiguate melancholia from non-melancholia and healthy controls on the basis of facial behavioural characteristics. We report the sensitivity and specificity of classification in depressive subtypes. These results serve as a baseline for more fine-grained depression classification and analysis.
|Name||2017 12TH IEEE INTERNATIONAL CONFERENCE ON AUTOMATIC FACE AND GESTURE RECOGNITION (FG 2017)|
|Conference||12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017) |
|Period||30/05/17 → 3/06/17|