Facial expression recognition (FER) is vital for human-computer interaction and emotion analysis, yet recognizing expressions in low-resolution images remains challenging. 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 FER model accuracy. Our framework comprises two main components: the Resolution Recognition Network (RRN) and the Multi-Resolution Adaptation Facial Expression Recognition Network (MRAFER). The RRN determines image resolution, outputs a binary vector, and the MRAFER assigns images to suitable facial expression recognition networks based on resolution. We evaluated DRGFER on widely-used datasets RAFDB and FERPlus, demonstrating that our method retains optimal model performance at each resolution and outperforms alternative resolution approaches. The proposed framework exhibits robustness against resolution variations and facial expressions, offering a promising solution for real-world applications.

翻译：面部表情识别（FER）对于人机交互和情感分析至关重要，然而在低分辨率图像中识别表情仍然具有挑战性。本文提出了一种称为动态分辨率引导的面部表情识别（DRGFER）的实用方法，旨在有效识别不同分辨率图像中的面部表情，同时不损害FER模型的准确性。我们的框架包含两个主要组件：分辨率识别网络（RRN）和多分辨率自适应面部表情识别网络（MRAFER）。RRN确定图像分辨率并输出一个二元向量，MRAFER则根据分辨率将图像分配给合适的面部表情识别网络。我们在广泛使用的数据集RAFDB和FERPlus上评估了DRGFER，结果表明我们的方法在各分辨率下均保持了最优的模型性能，并且优于其他分辨率处理方法。所提出的框架对分辨率变化和面部表情表现出鲁棒性，为实际应用提供了一个有前景的解决方案。