  The importance of automated Facial Emotion Recognition (FER) grows the more
common human-machine interactions become, which will only continue to increase
dramatically with time. A common method to describe human sentiment or feeling
is the categorical model the `7 basic emotions', consisting of `Angry',
`Disgust', `Fear', `Happiness', `Sadness', `Surprise' and `Neutral'. The
`Emotion Recognition in the Wild' (EmotiW) competition is now in its 7th year
and has become the standard benchmark for measuring FER performance. The focus
of this paper is the EmotiW sub-challenge of classifying videos in the `Acted
Facial Expression in the Wild' (AFEW) dataset, consisting of both visual and
audio modalities, into one of the above classes. Machine learning has exploded
as a research topic in recent years, with advancements in `Deep Learning' a key
part of this. Although Deep Learning techniques have been widely applied to the
FER task by entrants in previous years, this paper has two main contributions:
(i) to apply the latest `state-of-the-art' visual and temporal networks and
(ii) exploring various methods of fusing features extracted from the visual and
audio elements to enrich the information available to the final model making
the prediction. There are a number of complex issues that arise when trying to
classify emotions for `in-the-wild' video sequences, which the above two
approaches attempt to directly address. There are some positive findings when
comparing the results of this paper to past submissions, indicating that
further research into the proposed methods and fine-tuning of the models
deployed, could result in another step forwards in the field of automated FER.
