BiDepNet: A Bidirectional Enhancement Network for Depression Detection via Facial Expression Analysis

Abstract

Research has shown that facial expressions can effectively infer the severity of depression. Recently, depression assessment methods based on images or facial landmarks have gained widespread attention. However, existing methods have not fully considered the complementary and synergistic effects between facial landmark features and images in extracting depression cues. Therefore, this article proposes a bidirectional depression detection network, aimed at comprehensively capturing facial depression cues. We employ a differential feature enhancement module to extract the differences between features and inject them into the corresponding features. Simultaneously, the common feature enhancement module utilizes facial landmark-based structured positional information as a guiding signal, directing the model to focus on depression-related information within the facial image features and fuse shared features. Furthermore, through adaptive feature fusion, we enhance the expressive power of depression-related features to improve the accuracy of assessment. We conducted extensive experiments on the AVEC2014 depression dataset and the RAF-DB facial expression dataset. The experimental results demonstrate that our method outperforms the current state-of-the-art methods, providing strong evidence of its effectiveness.