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arXiv · 2003.00828

Verifying Deep Learning-based Decisions for Facial Expression Recognition

Ines Rieger·Rene Kollmann·Bettina Finzel·Dominik Seuss·Ute Schmid
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Abstract

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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Ines Rieger, Rene Kollmann, Bettina Finzel, Dominik Seuss, Ute Schmid. 2020-02-14. Verifying Deep Learning-based Decisions for Facial Expression Recognition. https://arxiv.org/abs/2003.00828

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