  Neural networks with high performance can still be biased towards
non-relevant features. However, reliability and robustness is especially
important for high-risk fields such as clinical pain treatment. We therefore
propose a verification pipeline, which consists of three steps. First, we
classify facial expressions with a neural network. Next, we apply layer-wise
relevance propagation to create pixel-based explanations. Finally, we quantify
these visual explanations based on a bounding-box method with respect to facial
regions. Although our results show that the neural network achieves
state-of-the-art results, the evaluation of the visual explanations reveals
that relevant facial regions may not be considered.
