  Depression is a common mental disorder that causes people to experience
depressed mood, loss of interest or pleasure, feelings of guilt or low
self-worth. Traditional clinical depression diagnosis methods are subjective
and time consuming. Since depression can be reflected by human facial
expressions, We propose a non-verbal facial behavior-based automatic depression
classification approach. In this paper, both short-term behavior-based and
clip-based depression classification are constructed. The final clip-level
decision of short-term behavior-based depression detection is yielded by
averaging the predictions of all short-term behaviors while we modelling
behaviors contained in all frames based on two Gaussian Mixture Models. To
evaluate the proposed approaches, we select a gender balanced subset from AVEC
2019 depression corpus containing 30 participants. The experimental results
show that our method achieved more than 75% depression classification accuracy,
where both GMM-based clip-level depression modelling and rank pooling-based
short-term depression behavior modelling achieved at least 70% classification
accuracy. The result indicates that our approach can leverage complementary
information from both systems to achieve promising depression predictions from
facial behaviors.
