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）在人机交互和情感分析中至关重要，但低分辨率图像下的表情识别仍具挑战性。本文提出一种名为"动态分辨率引导的面部表情识别"（DRGFER）的实用方法，旨在不降低FER模型精度的前提下有效识别不同分辨率图像中的面部表情。该框架包含两大组件：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用于判定图像分辨率并输出二值向量，MRAFER则根据分辨率将图像分配至合适的面部表情识别网络。我们在广泛使用的RAFDB和FERPlus数据集上评估了DRGFER，结果表明该方法能在各分辨率下保持最优模型性能，并优于其他分辨率处理方案。所提框架对分辨率变化和面部表情具有鲁棒性，为实际应用提供了具有前景的解决方案。