Sequential fusion of facial appearance and dynamics for depression recognition

In mental health assessment, it is validated that nonverbal cues like facial expressions can be indicative of depressive disorders. Recently, the multimodal fusion of facial appearance and dynamics based on convolutional neural networks has demonstrated encouraging performance in depression analysis. However, correlation and complementarity between different visual modalities have not been well studied in prior methods. In this paper, we propose a sequential fusion method for facial depression recognition. For mining the correlated and complementary depression patterns in multimodal learning, a chained-fusion mechanism is introduced to jointly learn facial appearance and dynamics in a unified framework. We show that such sequential fusion can provide a probabilistic perspective of the model correlation and complementarity between two different data modalities for improved depression recognition. Results on a benchmark dataset show the superiority of our method against several state-of-the-art alternatives.