Due to COVID-19, wearing masks has become more common. However, it is challenging to recognize expressions in the images of people wearing masks. In general facial recognition problems, blurred images and incorrect annotations of images in large-scale image datasets can make the model’s training difficult, which can lead to degraded recognition performance. To address this problem, this paper verifies the recognition ability of Self-Cure Network (SCN) on images of people wearing masks and proposes a self-adjustment module to further improve SCN (called SCN-SAM). We experimentally demonstrate the effectiveness of SCN on the masked facial expression dataset and demonstrate that SCN-SAM outperforms state-of-the-art methods in synthetic noise-added FER datasets.