  Neural networks are widely adopted, yet the integration of domain knowledge
is still underutilized. We propose to integrate domain knowledge about
co-occurring facial movements as a constraint in the loss function to enhance
the training of neural networks for affect recognition. As the co-ccurrence
patterns tend to be similar across datasets, applying our method can lead to a
higher generalizability of models and a lower risk of overfitting. We
demonstrate this by showing performance increases in cross-dataset testing for
various datasets. We also show the applicability of our method for calibrating
neural networks to different facial expressions.
