This study introduces LRDif, a novel diffusion-based framework designed
specifically for facial expression recognition (FER) within the context of
under-display cameras (UDC). To address the inherent challenges posed by UDC's
image degradation, such as reduced sharpness and increased noise, LRDif employs
a two-stage training strategy that integrates a condensed preliminary
extraction network (FPEN) and an agile transformer network (UDCformer) to
effectively identify emotion labels from UDC images. By harnessing the robust
distribution mapping capabilities of Diffusion Models (DMs) and the spatial
dependency modeling strength of transformers, LRDif effectively overcomes the
obstacles of noise and distortion inherent in UDC environments. Comprehensive
experiments on standard FER datasets including RAF-DB, KDEF, and FERPlus, LRDif
demonstrate state-of-the-art performance, underscoring its potential in
advancing FER applications. This work not only addresses a significant gap in
the literature by tackling the UDC challenge in FER but also sets a new
benchmark for future research in the field.

本研究引入了 LRDif，这是一个专为嵌入式显示相机（UDC）中的人脸表情识别（FER）而设计的新型扩散框架。LRDif 通过应用深度模型的鲁棒分布映射能力和变形器的空间依赖建模能力，以有效地识别 UDC 图像中的情绪标签，解决了 UDC 环境中噪声和畸变的困扰，并在包括 RAF-DB，KDEF 和 FERPlus 在内的标准 FER 数据集上展示了最先进的性能，突显了其在推动 FER 应用方面的潜力。这项工作不仅解决了 FER 领域中 UDC 挑战的重大差距，而且为未来的研究设定了新的基准。