  In this paper we explore the influence of some frequently used Convolutional
Neural Networks (CNNs), training settings, and training set structures, on
Action Unit (AU) detection. Specifically, we first compare 10 different shallow
and deep CNNs in AU detection. Second, we investigate how the different
training settings (i.e. centering/normalizing the inputs, using different
augmentation severities, and balancing the data) impact the performance in AU
detection. Third, we explore the effect of increasing the number of labelled
subjects and frames in the training set on the AU detection performance. These
comparisons provide the research community with useful tips about the choice of
different CNNs and training settings in AU detection. In our analysis, we use a
large-scale naturalistic dataset, consisting of ~55K videos captured in the
wild. To the best of our knowledge, there is no work that had investigated the
impact of such settings on a large-scale AU dataset.
