Deep Neural Networks for Depression Recognition Based on 2D and 3D Facial Expressions Under Emotional Stimulus Tasks - Guo, Weitong; Yang, Hongwu; Liu, Zhenyu; Xu, Yaping; Hu, Bin (Frontiers in Neuroscience, 2021)
The proportion of individuals with depression has rapidly increased along with the growth of the global population. Depression has been the currently most prevalent mental health disorder. An effective depression recognition system is especially crucial for automatic depression detection (ADD). A depression-related dataset is also critical during the evaluation of the system for ADD. Due to the sensitive nature of clinical data, availability and scale of such datasets are scarce. To our knowledge, there are few extensively practical depression datasets for the Chinese population. In this study, we firstly create a large-scale dataset by asking subjects to perform five mood elicitation tasks. After each task, subjects’ audio and video are collected, including 3D information (depth information) of facial expressions via a Kinect. The constructed dataset is from a real environment, i.e., several psychiatric hospitals, and has a specific scale. Then we propose a novel approach for depression recognition based on two kinds of different deep belief network (DBN) models. One model extracts 2D appearance features from facial images collected by an optical camera while the other model extracts 3D dynamic features from 3D facial points collected by a Kinect. The final decision result comes from the combination of the two models. Finally, we evaluate all proposed deep models on our built dataset. The experimental results demonstrate that (1) the recognition performance of combined 2D and 3D features model outperforms using either 2D or 3D features model only; (2) the performance of depression recognition is higher in the positive and negative emotional stimulus; (3) the recognition rate for females is generally higher than that for males. Meanwhile, we compare the performance with other methods on the same dataset. The experimental results show that our integrated 2D and 3D features deep belief network gives more accuracy than other methods, and the experimental paradigm designed for depression is reasonable and practical.