The purpose of facial expression recognition is to recognize the corresponding emotions. However, people tend to hide their emotions by displaying facial expressions that differ from those evoked by emotions. These inconsistent facial expressions are referred to as masked facial expressions (MFEs). The automatic recognition of hidden emotions within an MFE using image data is challenging. In this study, we find distinctive movement patterns in the facial action units (AUs) of MFE sequences through a detailed analysis. Considering our findings, we propose handcrafted features called dynamic AU intensity features (DAIFs) to represent AU movement. Furthermore, we develop a decoupled AU transformer (DAUT) model for recognition, where the decoupled convolution operators ensure that the temporal information in the DAIF is not damaged. To further improve the recognition performance, we design self-supervised clip prediction for pretraining of DAUT. Experimental results demonstrate that our proposed method performs exceptionally well across all tasks in the MFE dataset, particularly improving accuracy by nearly double on the most challenging 36-class task. This suggests that leveraging temporal information from facial AU movements is a reliable and effective technique for recognizing MFEs.