  Predicting affective information from human faces became a popular task for
most of the machine learning community in the past years. The development of
immense and dense deep neural networks was backed by the availability of
numerous labeled datasets. These models, most of the time, present
state-of-the-art results in such benchmarks, but are very difficult to adapt to
other scenarios. In this paper, we present one more chapter of benchmarking
different versions of the FaceChannel neural network: we demonstrate how our
little model can predict affective information from the facial expression on
the novel AffWild2 dataset.
