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

Facial expression recognition (FER) has recently attracted extensive attention in computer vision. However, existing methods mostly focus on the explicit performance and overlook their computational resources. Hence, achieving competitive performance while maintaining the model efficiency is still a huge challenge. To tackle these issues, we propose a highly lightweight yet effective Channel Shift-Enhancement Gabor-ResNet (CSE-GResNet) to capture the crucial visual properties in facial images. Concretely, we incorporate the Gabor Convolution (GConv) into ResNet to produce the robust GResNet as our backbone with limited memory cost. Furthermore, we propose extremely efficient Channel-Shift Module and Channel-Enhancement Module to insert in the GResNet in cascade. They are adopted to obtain and aggregate the facial informative representation from adjacent channels for extracting the subtle facial expression representation. We conduct extensive experiments on three wild datasets: RAF-DB, FER2013 and SFEW. The results show that the proposed CSE-GResNet achieves superior performance against the state-of-the-art methods with less computational and memory cost.