SDGSA: a lightweight shallow dual-group symmetric attention network for micro-expression recognition

Abstract

Recognizing micro-expressions (MEs) as subtle and transient forms of human emotional expressions is critical for accurately judging human feelings. However, recognizing MEs is challenging due to their transient and low-intensity characteristics. This study develops a lightweight shallow dual-group symmetric attention network (SDGSA) to address the limitations of existing methods in capturing the subtle features of MEs. This network takes the optical flow features as inputs, extracting ME features through a shallow network and performing finer feature segmentation in the channel dimension through a dual-group strategy. The goal is to focus on different types of facial information without disrupting facial symmetry. Moreover, this study implements a spatial symmetry attention module, focusing on extracting facial symmetry features to emphasize further the symmetric information of the left and right sides of the face. Additionally, we introduce the channel blending technique to optimize the information fusion between different channel features. Extensive experiments on SMIC, CASME II, SAMM, and 3DB-combined mainstream ME datasets demonstrate that the proposed SDGSA method outperforms the metrics of current state-of-the-art methods. As shown by ablation experimental results, the proposed dual-group symmetric attention module outperforms classical attention modules, such as the convolutional block attention module, squeeze-and-excitation, efficient channel attention, spatial group-wise enhancement, and multi-head self-attention. Importantly, SDGSA maintained excellent performance while having only 0.278 million parameters. The code and model are publicly available at https://github.com/YZY980123/SDGSA.