  In this paper, we investigate the impact of some of the commonly used
settings for (a) preprocessing face images, and (b) classification and
training, on Action Unit (AU) detection performance and complexity. We use in
our investigation a large-scale dataset, consisting of ~55K videos collected in
the wild for participants watching commercial ads. The preprocessing settings
include scaling the face to a fixed resolution, changing the color information
(RGB to gray-scale), aligning the face, and cropping AU regions, while the
classification and training settings include the kind of classifier
(multi-label vs. binary) and the amount of data used for training models. To
the best of our knowledge, no work had investigated the effect of those
settings on AU detection. In our analysis we use CNNs as our baseline
classification model.
