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Interpretable Deep Neural Networks for Dimensional and Categorical Emotion Recognition in-the-wild

2019-10-13 16:33:18
Xia Yicheng, Dimitrios Kollias
arXiv_CV
             
Abstract

Emotions play an important role in people's life. Understanding and recognising is not only important for interpersonal communication, but also has promising applications in Human-Computer Interaction, automobile safety and medical research. This project focuses on extending the emotion recognition database, and training the CNN + RNN emotion recognition neural networks with emotion category representation and valence \& arousal representation. The combined models are constructed by training the two representations simultaneously. The comparison and analysis between the three types of model are discussed. The inner-relationship between two emotion representations and the interpretability of the neural networks are investigated. The findings suggest that categorical emotion recognition performance can benefit from training with a combined model. And the mapping of emotion category and valence \& arousal values can explain this phenomenon.

Abstract (translated)

URL

https://arxiv.org/abs/1910.05784

PDF

https://arxiv.org/pdf/1910.05784.pdf

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