Semi-Supervised Facial Expression Recognition based on Dynamic Threshold and Negative Learning

TL;DR

This paper introduces a semi-supervised facial expression recognition method that leverages dynamic thresholding and negative learning to improve performance using limited labeled data, achieving state-of-the-art results.

Contribution

It proposes a novel semi-supervised framework with dynamic threshold adjustment and selective negative learning for facial expression recognition.

Findings

Achieved state-of-the-art performance on RAF-DB and AffectNet datasets.

Surpassed fully supervised methods without using the entire dataset.

Demonstrated effectiveness of local attention and dropout strategies.

Abstract

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.