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Facial emotion recognition is the task to classify human emotions in faceimages. It is a difficult task due to high aleatoric uncertainty and visualambiguity. A large part of the literature aims to show progress by increasingaccuracy on this task, but this ignores the inherent uncertainty and ambiguityin the task. In this paper we show that Bayesian Neural Networks, asapproximated using MC-Dropout, MC-DropConnect, or an Ensemble, are able tomodel the aleatoric uncertainty in facial emotion recognition, and produceoutput probabilities that are closer to what a human expects. We also show thatcalibration metrics show strange behaviors for this task, due to the multipleclasses that can be considered correct, which motivates future work. We believeour work will motivate other researchers to move away from Classical and intoBayesian Neural Networks.

Hey Human, If your Facial Emotions are Uncertain, You Should Use Bayesian Neural Networks!