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arXiv · 2210.17233

CorrLoss: Integrating Co-Occurrence Domain Knowledge for Affect Recognition

Ines Rieger·Jaspar Pahl·Bettina Finzel·Ute Schmid
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Abstract

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.

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Ines Rieger, Jaspar Pahl, Bettina Finzel, Ute Schmid. 2022-10-31. CorrLoss: Integrating Co-Occurrence Domain Knowledge for Affect Recognition. https://doi.org/10.1109/icpr56361.2022.9956319

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