High Semantic Features for the Continual Learning of Complex Emotions: a Lightweight Solution
This addresses incremental learning challenges in emotion recognition, offering a lightweight solution, though it appears incremental in nature.
The paper tackles catastrophic forgetting in incremental learning for complex emotion recognition by using Action Units as non-transient, semantic features, achieving an accuracy of 0.75 on the CFEE dataset and resulting in a lightweight model.
Incremental learning is a complex process due to potential catastrophic forgetting of old tasks when learning new ones. This is mainly due to transient features that do not fit from task to task. In this paper, we focus on complex emotion recognition. First, we learn basic emotions and then, incrementally, like humans, complex emotions. We show that Action Units, describing facial muscle movements, are non-transient, highly semantical features that outperform those extracted by both shallow and deep convolutional neural networks. Thanks to this ability, our approach achieves interesting results when learning incrementally complex, compound emotions with an accuracy of 0.75 on the CFEE dataset and can be favorably compared to state-of-the-art results. Moreover, it results in a lightweight model with a small memory footprint.