  Facial emotion recognition is the task to classify human emotions in face
images. It is a difficult task due to high aleatoric uncertainty and visual
ambiguity. A large part of the literature aims to show progress by increasing
accuracy on this task, but this ignores the inherent uncertainty and ambiguity
in the task. In this paper we show that Bayesian Neural Networks, as
approximated using MC-Dropout, MC-DropConnect, or an Ensemble, are able to
model the aleatoric uncertainty in facial emotion recognition, and produce
output probabilities that are closer to what a human expects. We also show that
calibration metrics show strange behaviors for this task, due to the multiple
classes that can be considered correct, which motivates future work. We believe
our work will motivate other researchers to move away from Classical and into
Bayesian Neural Networks.
