Suppressing Uncertainties for Large-Scale Facial Expression Recognition

Code

• github.com/kaiwang960112/Self-Cure-Network
  OfficialIn paperpytorch★ 423
• github.com/RainbowRui/Landmark-Driven-Facial-Expression-Recognition
  pytorch★ 186

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 88.14\% on RAF-DB, 60.23\% on AffectNet, and 89.35\% on FERPlus. The code will be available at https://github.com/kaiwang960112/Self-Cure-Network.

Tasks

Reproductions