Two-stream Global-Guided Attention Network for Facial Expression Recognition

Abstract

Facial expression recognition (FER) in the wild is an important yet challenging problem because of uncontrolled conditions, such as occlusions, pose, and illumination. Most existing methods utilize the global and local information, but ignore the potential correlation between the global and local faces. In this paper, we propose a two-stream global-guided attention network (TGGAN) for FER in the wild. To further exploit the complementary relationship between local and global, we design a global-guided attention module (GGAM). Especially, a global guidance mechanism is proposed in GGAM to utilize the global information to guide the capture of key local features. Furthermore, inspired by the transformer, a self-attention mechanism is introduced in GGAM to emphasize salient face regions and fully integrate features extracted from global and local streams. We validate the proposed TGGAN on two wild datasets (FERPlus, RAF -DB) and further conduct experiments on their test subsets of occlusion and multi-poses. Extensive experiments show that the proposed TGGAN achieves superior performance against the state-of-the-art methods.