  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.

Machine Learning;Computer Vision and Pattern Recognition;Machine Learning