Choose Settings Carefully: Comparing Action Unit detection at Different Settings Using a Large-Scale Dataset
This work addresses the need for better AU detection settings in facial expression analysis, but it is incremental as it focuses on comparing existing methods rather than introducing new ones.
The paper tackles the problem of optimizing Action Unit detection by comparing the impact of preprocessing and training settings on performance and complexity, using a large-scale dataset of ~55K videos, and finds that careful selection of settings can improve results, though specific numbers are not provided.
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.