Do Deep Neural Networks Forget Facial Action Units? -- Exploring the Effects of Transfer Learning in Health Related Facial Expression Recognition
Pooja Prajod, Dominik Schiller, Tobias Huber, Elisabeth Andr\'e

TL;DR
This study investigates how transfer learning affects facial action unit recognition, revealing that fine-tuning for pain recognition causes neural networks to forget certain emotion-related features.
Contribution
It introduces a comprehensive method combining transfer learning, Layer-wise Relevance Propagation, and concept embedding analysis to study feature forgetting in facial expression recognition.
Findings
Networks forget emotion-related action units after fine-tuning for pain recognition.
Fine-tuning reduces attention to specific facial features relevant for emotion recognition.
The approach provides insights into how transfer learning impacts model interpretability.
Abstract
In this paper, we present a process to investigate the effects of transfer learning for automatic facial expression recognition from emotions to pain. To this end, we first train a VGG16 convolutional neural network to automatically discern between eight categorical emotions. We then fine-tune successively larger parts of this network to learn suitable representations for the task of automatic pain recognition. Subsequently, we apply those fine-tuned representations again to the original task of emotion recognition to further investigate the differences in performance between the models. In the second step, we use Layer-wise Relevance Propagation to analyze predictions of the model that have been predicted correctly previously but are now wrongly classified. Based on this analysis, we rely on the visual inspection of a human observer to generate hypotheses about what has been forgotten…
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