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The importance of automated Facial Emotion Recognition (FER) grows the morecommon human-machine interactions become, which will only continue to increasedramatically with time. A common method to describe human sentiment or feelingis 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 yearand has become the standard benchmark for measuring FER performance. The focusof this paper is the EmotiW sub-challenge of classifying videos in the `ActedFacial Expression in the Wild' (AFEW) dataset, consisting of both visual andaudio modalities, into one of the above classes. Machine learning has explodedas a research topic in recent years, with advancements in `Deep Learning' a keypart of this. Although Deep Learning techniques have been widely applied to theFER 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 andaudio elements to enrich the information available to the final model makingthe prediction. There are a number of complex issues that arise when trying toclassify emotions for `in-the-wild' video sequences, which the above twoapproaches attempt to directly address. There are some positive findings whencomparing the results of this paper to past submissions, indicating thatfurther research into the proposed methods and fine-tuning of the modelsdeployed, could result in another step forwards in the field of automated FER.

AI in Pursuit of Happiness, Finding Only Sadness: Multi-Modal Facial Emotion Recognition Challenge