Combating Uncertainty and Class Imbalance in Facial Expression Recognition

TL;DR

This paper introduces a ResNet and Attention-based framework that addresses both class imbalance and uncertainty in facial expression recognition, improving accuracy and robustness on datasets like AffectNet and RAF-DB.

Contribution

The novel framework combines class-specific weighting and uncertain feature learning to simultaneously tackle class imbalance and uncertainty in facial expression recognition.

Findings

Outperforms basic methods on AffectNet and RAF-DB datasets

Effectively handles class imbalance through weighted classes and penalty mechanisms

Reduces uncertainty by mixing uncertain features between samples

Abstract

Recognition of facial expression is a challenge when it comes to computer vision. The primary reasons are class imbalance due to data collection and uncertainty due to inherent noise such as fuzzy facial expressions and inconsistent labels. However, current research has focused either on the problem of class imbalance or on the problem of uncertainty, ignoring the intersection of how to address these two problems. Therefore, in this paper, we propose a framework based on Resnet and Attention to solve the above problems. We design weight for each class. Through the penalty mechanism, our model will pay more attention to the learning of small samples during training, and the resulting decrease in model accuracy can be improved by a Convolutional Block Attention Module (CBAM). Meanwhile, our backbone network will also learn an uncertain feature for each sample. By mixing uncertain features between samples, the model can better learn those features that can be used for classification, thus suppressing uncertainty. Experiments show that our method surpasses most basic methods in terms of accuracy on facial expression data sets (e.g., AffectNet, RAF-DB), and it also solves the problem of class imbalance well.