  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.
