  Facial Expression Recognition (FER) is an active research domain that has
shown great progress recently, notably thanks to the use of large deep learning
models. However, such approaches are particularly energy intensive, which makes
their deployment difficult for edge devices. To address this issue, Spiking
Neural Networks (SNNs) coupled with event cameras are a promising alternative,
capable of processing sparse and asynchronous events with lower energy
consumption. In this paper, we establish the first use of event cameras for
FER, named "Event-based FER", and propose the first related benchmarks by
converting popular video FER datasets to event streams. To deal with this new
task, we propose "Spiking-FER", a deep convolutional SNN model, and compare it
against a similar Artificial Neural Network (ANN). Experiments show that the
proposed approach achieves comparable performance to the ANN architecture,
while consuming less energy by orders of magnitude (up to 65.39x). In addition,
an experimental study of various event-based data augmentation techniques is
performed to provide insights into the efficient transformations specific to
event-based FER.
