It is difficult to highlight the features of facial expressions in the study of global faces, due to the unique subtleties and complexity of facial expressions. To improve the robustness of expression recognition in natural environments and optimize model parameters, this paper proposes a lightweight facial expression recognition method based on multiregion fusion, which integrates local details and global features to realize a combination of coarse and fine granularity, thus improving the model's efficacy in discriminating subtle changes in expressions. First, local features are extracted from the human face through a branch, which uses eyes and mouth as input. Second, the facial global features are adaptively acquired by another branch, and a mask is generated by key points to assist in adjusting the facial attention map. The facial attention map acts on the global features to highlight the weight of the unmasked parts and describes the overall high-level semantic information. A pruning algorithm is used to perform lightweight optimization for the overall model, using less memory and few computational operations to obtain a more compact network. The recognition accuracy of the proposed method on RAF-DB and AffectNet datasets is determined to be 85.39% and 58.81%, respectively. The experimental results show that the recognition accuracy of the proposed method is higher than other advanced methods and the proposed method significantly reduces the number of parameters, which proves the effectiveness and progressiveness.