Facial expression recognition (FER) plays a crucial role in human-computer interaction and emotion analysis. However, recognizing expressions in low-resolution images remains a significant challenge. This paper introduces a practical method called Dynamic Resolution Guidance for Facial Expression Recognition (DRGFER) to effectively recognize facial expressions in images with varying resolutions without compromising the accuracy of the FER model. Our framework comprises two main components: the Resolution Recognition Network (RRN) and the Multi-Resolution Adaptation Facial Expression Recognition Network (MRAFER). The RRN determines the resolution of the input image, and the MRAFER assigns the image to the most suitable facial expression recognition network according to its resolution. We evaluated the performance of DRGFER on two widely used datasets, RAF-DB and FERPlus. The results demonstrate that our method maintains optimal model performance at each resolution and outperforms alternative resolution-handling approaches. The proposed framework exhibits robustness against variations in both resolution and facial expressions, offering a promising solution for real-world applications.