DepNet

| openaire: EC/H2020/101016775/EU//INTERVENE Funding Information: This study is supported by the Scientific Research Program Funded by Shaanxi Provincial Education Department (Program No. 20JG030), the Special Construction Fund for Key Disciplines of Shaanxi Provincial Higher Education. This study was supported by the Academy of Finland (grants 336033 and 315896), Business Finland (grant 884/31/2018), and EU H2020 (grant 101016775). Publisher Copyright: © 2021 Wiley Periodicals LLCAs a common mental disorder, depression has attracted many researchers from affective computing field to estimate the depression severity. However, existing approaches based on Deep Learning (DL) are mainly focused on single facial image without considering the sequence information for predicting the depression scale. In this paper, an integrated framework, termed DepNet, for automatic diagnosis of depression that adopts facial images sequence from videos is proposed. Specifically, several pretrained models are adopted to represent the low-level features, and Feature Aggregation Module is proposed to capture the high-level characteristic information for depression analysis. More importantly, the discriminative characteristic of depression on faces can be mined to assist the clinicians to diagnose the severity of the depressed subjects. Multiscale experiments carried out on AVEC2013 and AVEC2014 databases have shown the excellent performance of the intelligent approach. The root mean-square error between the predicted values and the Beck Depression Inventory-II scores is 9.17 and 9.01 on the two databases, respectively, which are lower than those of the state-of-the-art video-based depression recognition methods.Peer reviewe