Deep Neural Networks for Depression Recognition Based on Facial Expressions Caused by Stimulus Tasks

With the growth of the global population, the proportion of individuals with depression has rapidly increased; it is currently the most prevalent mental health disorder. Although existing studies on depression have mainly examined the several databases, which comprise facial images and videos of non-Chinese subjects, there are few effective databases for a Chinese population. In this study, we first create a depression database by asking participants to perform five mood-elicitation tasks. After each task, their facial expressions are collected via a Kinect. In the depression database, the facial feature points (FFP) and facial action units (AU) are obtained. We build a range of deep belief network (DBN) models based on FFPs and AUs to extract facial features from facial expressions, named 5DBN, AU-5DBN and 5DBN-AU. We evaluate all proposed models in our built database, and the results demonstrate that (1) the recognition performance of the AU-5DBN model is higher than that of the 5DBN-AU model, and that of the single feature model is the lowest; (2) The performance of depression recognition in the positive and negative emotional stimuluses are higher than that of neutral emotional stimulus; (3) The classification rate for females is generally higher than that for males. Most importantly, the constructed database is from a real environment, i.e., several psychiatric hospitals, and has a certain scale. The experimental results show higher recognition performance in the database; thus, the proposed method is validated as effective in identifying depression.