DepressionMLP: A Multi-Layer Perceptron Architecture for Automatic Depression Level Prediction via Facial Keypoints and Action Units

Physiological studies have confirmed that there are differences in facial activities between depressed and healthy individuals. Therefore, while protecting the privacy of subjects, substantial efforts are made to predict the depression severity of individuals by analyzing Facial Keypoints Representation Sequences (FKRS) and Action Units Representation Sequences (AURS). However, those works has struggled to examine the spatial distribution and temporal changes of Facial Keypoints (FKs) and Action Units (AUs) simultaneously, which is limited in extracting the facial dynamics characterizing depressive cues. Besides, those works don't realize the complementarity of effective information extracted from FKRS and AURS, which reduces the prediction accuracy. To this end, we intend to use the recently proposed Multi-Layer Perceptrons with gating (gMLP) architecture to process FKRS and AURS for predicting depression levels. However, the channel projection in the gMLP disrupts the spatial distribution of FKs and AUs, leading to input and output sequences not having the same spatiotemporal attributes. This discrepancy hinders the additivity of residual connections in a physical sense. Therefore, we construct a novel MLP architecture named DepressionMLP. In this model, we propose the Dual Gating (DG) and Mutual Guidance (MG) modules. The DG module embeds cross-location and cross-frame gating results into the input sequence to maintain the physical properties of data to make up for the shortcomings of gMLP. The MG module takes the global information of FKRS (AURS) as a guidance mask to filter the AURS (FKRS) to achieve the interaction between FKRS and AURS. Experimental results on several benchmark datasets show the effectiveness of our method.