Deep Spiking Neural Network for High-Accuracy and Energy-Efficient Face Action Unit Recognition

In recent years, spiking neural networks (SNNs) have received significant attention as the third-generation of networks due to their event-driven and low-powered nature. However, their applications have been limited to relatively simple tasks such as image classification, since it is difficult to train SNNs and converting deep artificial neural networks (ANNs) into SNNs directly usually causes large accuracy degradation. In this paper, we employ an SNN to solve a more challenging multi-label classification task and propose the first spiking-based network for face action unit (AU) recognition. Specifically, a relation extracting module based on graph convolution network (GCN) is proposed to leverage AU regional features. Channel-wise normalization methods for residual blocks of the Resnet backbone and GCN blocks are proposed for ANN-to-SNN conversion to keep the high performance. Experiments on the BP4D dataset show that our proposed model achieves high-accuracy performance, and converges 3 times faster than previous methods.