Verifying Deep Learning based Decisions for Facial Expression Recognition
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.
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