Spiking-Fer: Spiking Neural Network for Facial Expression Recognition With Event Cameras
This work addresses energy efficiency for edge devices in facial expression recognition, though it is incremental as it adapts existing SNN methods to a new application.
The paper tackles the problem of high energy consumption in facial expression recognition (FER) by proposing Spiking-FER, a spiking neural network for event-based FER, which achieves comparable performance to artificial neural networks while reducing energy consumption by up to 65.39x.
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.