Suppressing Uncertainties for Large-Scale Facial Expression Recognition

TL;DR

This paper introduces a Self-Cure Network (SCN) that effectively suppresses uncertainties in large-scale facial expression recognition, improving accuracy by weighting samples and relabeling uncertain data.

Contribution

The novel SCN method combines self-attention and relabeling mechanisms to address uncertainties in FER, enhancing deep learning performance on challenging datasets.

Findings

SCN outperforms state-of-the-art methods on multiple FER benchmarks.

Achieves 88.14% accuracy on RAF-DB, 60.23% on AffectNet, 89.35% on FERPlus.

Validated effectiveness on synthetic and real-world datasets.

Abstract

Annotating a qualitative large-scale facial expression dataset is extremely difficult due to the uncertainties caused by ambiguous facial expressions, low-quality facial images, and the subjectiveness of annotators. These uncertainties lead to a key challenge of large-scale Facial Expression Recognition (FER) in deep learning era. To address this problem, this paper proposes a simple yet efficient Self-Cure Network (SCN) which suppresses the uncertainties efficiently and prevents deep networks from over-fitting uncertain facial images. Specifically, SCN suppresses the uncertainty from two different aspects: 1) a self-attention mechanism over mini-batch to weight each training sample with a ranking regularization, and 2) a careful relabeling mechanism to modify the labels of these samples in the lowest-ranked group. Experiments on synthetic FER datasets and our collected WebEmotion dataset validate the effectiveness of our method. Results on public benchmarks demonstrate that our SCN outperforms current state-of-the-art methods with \textbf{88.14}\% on RAF-DB, \textbf{60.23}\% on AffectNet, and \textbf{89.35}\% on FERPlus. The code will be available at \href{https://github.com/kaiwang960112/Self-Cure-Network}{https://github.com/kaiwang960112/Self-Cure-Network}.