FaceMixup: Enhancing Facial Expression Recognition through Mixed Face Regularization

TL;DR

This paper introduces FaceMixup, a novel data augmentation method for facial expression recognition that uses mixed face component regularization to improve model performance over traditional augmentation techniques.

Contribution

The paper proposes a new face data augmentation approach called FaceMixup, which outperforms existing methods like MixAugment on standard FER datasets.

Findings

FaceMixup achieves higher accuracy than classical DA methods.

It outperforms MixAugment on FER datasets.

The approach enhances generalization in facial expression recognition.

Abstract

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.