This paper explores static facial expression recognition (FER) and presents a novel facial augmentation technique designed to enhance model training. By utilizing pretrained facial landmark detection models, we analyze the spatial structure of faces within the FER training dataset. Based on the predicted landmark coordinates, facial images are augmented by strategically masking patches of varying sizes at key landmark locations. This approach emphasizes the structural significance of facial landmarks while preserving other critical facial features, enabling models to capture both global facial structure and nuanced expression-related details. Extensive experiments on benchmark datasets validate the effectiveness of the proposed method, showcasing its potential to improve FER performance, particularly in challenging scenarios.