The proliferation of deep learning solutions and the scarcity of large annotated datasets pose significant challenges in real-world applications. Various strategies have been explored to overcome this challenge, with data augmentation (DA) approaches emerging as prominent solutions. DA approaches involve generating additional examples by transforming existing labeled data, thereby enriching the dataset and helping deep learning models achieve improved generalization without succumbing to overfitting. In real applications, where solutions based on deep learning are widely used, there is facial expression recognition (FER), which plays an essential role in human communication, improving a range of knowledge areas (e.g., medicine, security, and marketing). In this paper, we propose a simple and comprehensive face data augmentation approach based on mixed face component regularization that outperforms the classical DA approaches from the literature, including the MixAugment which is a specific approach for the target task in two well-known FER datasets existing in the literature.

翻译：深度学习解决方案的激增与大规模标注数据集的稀缺性，为实际应用带来了重大挑战。为克服这一难题，研究者们探索了多种策略，其中数据增强方法已成为主流解决方案。数据增强方法通过对现有标注数据进行变换以生成额外样本，从而丰富数据集，并帮助深度学习模型在不陷入过拟合的情况下实现更好的泛化能力。在深度学习解决方案广泛应用的现实场景中，面部表情识别作为人类沟通的关键环节，对医学、安防、营销等多个知识领域的发展具有重要推动作用。本文提出了一种基于混合面部组件正则化的简洁而全面的面部数据增强方法，该方法在文献中两个知名面部表情识别数据集上的表现超越了传统数据增强方法，包括针对该目标任务的特定方法MixAugment。