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Facial Expression Recognition (FER) is an active research domain that hasshown great progress recently, notably thanks to the use of large deep learningmodels. However, such approaches are particularly energy intensive, which makestheir deployment difficult for edge devices. To address this issue, SpikingNeural Networks (SNNs) coupled with event cameras are a promising alternative,capable of processing sparse and asynchronous events with lower energyconsumption. In this paper, we establish the first use of event cameras forFER, named "Event-based FER", and propose the first related benchmarks byconverting popular video FER datasets to event streams. To deal with this newtask, we propose "Spiking-FER", a deep convolutional SNN model, and compare itagainst a similar Artificial Neural Network (ANN). Experiments show that theproposed 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 isperformed to provide insights into the efficient transformations specific toevent-based FER.

Spiking-Fer: Spiking Neural Network for Facial Expression Recognition With Event Cameras