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.

翻译：本研究提出了LRDif，一种专为屏下摄像头（UDC）环境下的面部表情识别（FER）设计的新型扩散模型框架。为解决UDC图像退化（如清晰度下降和噪声增加）带来的固有挑战，LRDif采用两阶段训练策略，整合了紧凑的初步提取网络（FPEN）和敏捷变换器网络（UDCformer），以有效识别UDC图像中的情感标签。通过利用扩散模型（DMs）强大的分布映射能力以及Transformer的空间依赖性建模优势，LRDif有效克服了UDC环境中固有的噪声和失真障碍。在包含RAF-DB、KDEF和FERPlus的标准FER数据集上的综合实验表明，LRDif展现了最先进的性能，彰显了其在推进FER应用方面的潜力。这项工作不仅通过应对FER中的UDC挑战填补了文献中的一个重要空白，而且为该领域的未来研究树立了新的基准。