  Recognizing faces and their underlying emotions is an important aspect of
biometrics. In fact, estimating emotional states from faces has been tackled
from several angles in the literature. In this paper, we follow the novel route
of using neuromorphic data to predict valence and arousal values from faces.
Due to the difficulty of gathering event-based annotated videos, we leverage an
event camera simulator to create the neuromorphic counterpart of an existing
RGB dataset. We demonstrate that not only training models on simulated data can
still yield state-of-the-art results in valence-arousal estimation, but also
that our trained models can be directly applied to real data without further
training to address the downstream task of emotion recognition. In the paper we
propose several alternative models to solve the task, both frame-based and
video-based.
