  Deep learning models have shown promising results in recognizing depressive
states using video-based facial expressions. While successful models typically
leverage using 3D-CNNs or video distillation techniques, the different use of
pretraining, data augmentation, preprocessing, and optimization techniques
across experiments makes it difficult to make fair architectural comparisons.
We propose instead to enhance two simple models based on ResNet-50 that use
only static spatial information by using two specific face alignment methods
and improved data augmentation, optimization, and scheduling techniques. Our
extensive experiments on benchmark datasets obtain similar results to
sophisticated spatio-temporal models for single streams, while the score-level
fusion of two different streams outperforms state-of-the-art methods. Our
findings suggest that specific modifications in the preprocessing and training
process result in noticeable differences in the performance of the models and
could hide the actual originally attributed to the use of different neural
network architectures.
