Facial expression recognition is a key task in human-computer interaction and affective computing. However, acquiring a large amount of labeled facial expression data is often costly. Therefore, it is particularly important to design a semi-supervised facial expression recognition algorithm that makes full use of both labeled and unlabeled data. In this paper, we propose a semi-supervised facial expression recognition algorithm based on Dynamic Threshold Adjustment (DTA) and Selective Negative Learning (SNL). Initially, we designed strategies for local attention enhancement and random dropout of feature maps during feature extraction, which strengthen the representation of local features while ensuring the model does not overfit to any specific local area. Furthermore, this study introduces a dynamic thresholding method to adapt to the requirements of the semi-supervised learning framework for facial expression recognition tasks, and through a selective negative learning strategy, it fully utilizes unlabeled samples with low confidence by mining useful expression information from complementary labels, achieving impressive results. We have achieved state-of-the-art performance on the RAF-DB and AffectNet datasets. Our method surpasses fully supervised methods even without using the entire dataset, which proves the effectiveness of our approach.

翻译：面部表情识别是人机交互与情感计算中的关键任务。然而，获取大量带标签的面部表情数据通常成本高昂。因此，设计一种能够充分利用有标签与无标签数据的半监督面部表情识别算法显得尤为重要。本文提出了一种基于动态阈值调整（DTA）与选择性负学习（SNL）的半监督面部表情识别算法。首先，我们在特征提取阶段设计了局部注意力增强与特征图随机丢弃的策略，在强化局部特征表示的同时，确保模型不会过度拟合任何特定的局部区域。此外，本研究引入了一种动态阈值方法，以适应半监督学习框架对面部表情识别任务的要求，并通过选择性负学习策略，从互补标签中挖掘有用的表情信息，从而充分利用低置信度的无标签样本，取得了令人印象深刻的结果。我们在RAF-DB和AffectNet数据集上实现了最先进的性能。即使未使用整个数据集，我们的方法也超越了全监督方法，这证明了我们方法的有效性。