CS-GResNet: A Simple and Highly Efficient Network for Facial Expression Recognition

Abstract

Facial expression recognition (FER) has recently attracted attention in computer vision. However, existing methods mostly focus on the explicit performance and overlook their computational resources and memory consumption. Hence, achieving promising performance while maintaining the efficiency of models is still a huge challenge. In this work, we propose a highly efficient Channel-Shift Gabor-ResNet (CS-GResNet) to capture the crucial visual properties in facial images. Concretely, we incorporate the Gabor Convolution (GConv) into ResNet to produce the significant GResNet as our backbone with limited memory cost. Furthermore, we adopt an extremely simple yet effective Channel-Shift Module inserted into the GResNet to obtain the facial informative representation via facilitating information exchanged among neighboring channels. We conduct extensive experiments on three wild datasets: RAF-DB, FER2013 and SFEW. The results show that our proposed CS-GResNet achieves superior performance against the state-of-the-art methods with less computational and memory cost. Codes are available at https://github.com/jsesr/CS-GResNet-PyTorch.