A Personalized Affective Memory Neural Model for Improving Emotion Recognition
This addresses the challenge of person-specific affective understanding in emotion recognition, which is incremental as it builds on existing deep learning methods.
The paper tackles the problem of unreliable emotion recognition for personalized facial expressions by introducing a neural model that combines a conditional adversarial autoencoder with Grow-When-Required networks as affective memories, achieving state-of-the-art performance on in-the-wild datasets.
Recent models of emotion recognition strongly rely on supervised deep learning solutions for the distinction of general emotion expressions. However, they are not reliable when recognizing online and personalized facial expressions, e.g., for person-specific affective understanding. In this paper, we present a neural model based on a conditional adversarial autoencoder to learn how to represent and edit general emotion expressions. We then propose Grow-When-Required networks as personalized affective memories to learn individualized aspects of emotion expressions. Our model achieves state-of-the-art performance on emotion recognition when evaluated on \textit{in-the-wild} datasets. Furthermore, our experiments include ablation studies and neural visualizations in order to explain the behavior of our model.