A Personalized Affective Memory Model for Improving Emotion Recognition

Pablo Barros, German Parisi, Stefan Wermter
Proceedings of the 36th International Conference on Machine Learning, PMLR 97:485-494, 2019.

Abstract

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 emotional expressions. Our model achieves state-of-the-art performance on emotion recognition when evaluated on in-the-wild datasets. Furthermore, our experiments include ablation studies and neural visualizations in order to explain the behavior of our model.

Cite this Paper

BibTeX
@InProceedings{pmlr-v97-barros19a, title = {A Personalized Affective Memory Model for Improving Emotion Recognition}, author = {Barros, Pablo and Parisi, German and Wermter, Stefan}, booktitle = {Proceedings of the 36th International Conference on Machine Learning}, pages = {485--494}, year = {2019}, editor = {Chaudhuri, Kamalika and Salakhutdinov, Ruslan}, volume = {97}, series = {Proceedings of Machine Learning Research}, month = {09--15 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v97/barros19a/barros19a.pdf}, url = {https://proceedings.mlr.press/v97/barros19a.html}, abstract = {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 emotional expressions. Our model achieves state-of-the-art performance on emotion recognition when evaluated on in-the-wild datasets. Furthermore, our experiments include ablation studies and neural visualizations in order to explain the behavior of our model.} }
Endnote
%0 Conference Paper %T A Personalized Affective Memory Model for Improving Emotion Recognition %A Pablo Barros %A German Parisi %A Stefan Wermter %B Proceedings of the 36th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2019 %E Kamalika Chaudhuri %E Ruslan Salakhutdinov %F pmlr-v97-barros19a %I PMLR %P 485--494 %U https://proceedings.mlr.press/v97/barros19a.html %V 97 %X 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 emotional expressions. Our model achieves state-of-the-art performance on emotion recognition when evaluated on in-the-wild datasets. Furthermore, our experiments include ablation studies and neural visualizations in order to explain the behavior of our model.
APA
Barros, P., Parisi, G. & Wermter, S.. (2019). A Personalized Affective Memory Model for Improving Emotion Recognition. Proceedings of the 36th International Conference on Machine Learning, in Proceedings of Machine Learning Research 97:485-494 Available from https://proceedings.mlr.press/v97/barros19a.html.

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