  Recent studies show that depression can be partially reflected from human
facial attributes. Since facial attributes have various data structure and
carry different information, existing approaches fail to specifically consider
the optimal way to extract depression-related features from each of them, as
well as investigates the best fusion strategy. In this paper, we propose to
extend Neural Architecture Search (NAS) technique for designing an optimal
model for multiple facial attributes-based depression recognition, which can be
efficiently and robustly implemented in a small dataset. Our approach first
conducts a warmer up step to the feature extractor of each facial attribute,
aiming to largely reduce the search space and providing customized
architecture, where each feature extractor can be either a Convolution Neural
Networks (CNN) or Graph Neural Networks (GNN). Then, we conduct an end-to-end
architecture search for all feature extractors and the fusion network, allowing
the complementary depression cues to be optimally combined with less
redundancy. The experimental results on AVEC 2016 dataset show that the model
explored by our approach achieves breakthrough performance with 27\% and 30\%
RMSE and MAE improvements over the existing state-of-the-art. In light of these
findings, this paper provides solid evidences and a strong baseline for
applying NAS to time-series data-based mental health analysis.
