Deep Learning for Depression Recognition with Audiovisual Cues: A Review

Abstract

With the acceleration of the pace of work and life, people have to face more and more pressure, which increases the possibility of suffering from depression. However, many patients may fail to get a timely diagnosis due to the serious imbalance in the doctor-patient ratio in the world. Promisingly, physiological and psychological studies have indicated some differences in speech and facial expression between patients with depression and healthy individuals. Consequently, to improve current medical care, many scholars have used deep learning to extract a representation of depression cues in audio and video for automatic depression detection. To sort out and summarize these works, this review introduces the databases and describes objective markers for automatic depression estimation (ADE). Furthermore, we review the deep learning methods for automatic depression detection to extract the representation of depression from audio and video. Finally, this paper discusses challenges and promising directions related to automatic diagnosing of depression using deep learning technologies.

DepNet: An automated industrial intelligent system using deep learning for video‐based depression analysis

“…The aim of the present paper is to propose a unified architecture for designing an automatic protosystem capable of efficiently assessing depression severity (i.e., BDI-II Score) given a sampled facial image sequence from video. On the basis of the novel advances in DL field and facial expression analysis, [22][23][24] it is proposed to use many DL approaches (deep convolutional neural network [DCNN], long-short term memory [LSTM], etc.) to learn high-level semantics features from the facial image sequences.…”