通过遮蔽面部特征，发现某些表情识别反而更准，提出新训练方案提升性能。
Leaving Some Facial Features Behind
- 用遮蔽法测试面部特征对情绪分类的影响
- 移除关键特征后整体准确率下降最高达85%
- 针对厌恶表情的遮蔽反而提升准确率，启发新训练策略
面部表情是人类交流的关键，反映情绪状态。本研究在Fer2013数据集上使用面部扰动，分析特定面部特征对情绪分类的影响。如预期，移除重要特征后，对快乐和惊讶等情绪的分类准确率最高下降85%。然而，令人意外的是，对于厌恶情绪，应用遮蔽后分类器准确率略有提升。基于此现象，我们提出了新型的Perturb训练方案，包含注意力分类、像素聚类和特征聚焦三个阶段，实验表明该方法能提升情绪分类准确率。结果表明，在情绪识别任务中，适度移除个别面部特征存在潜在优势。
原文摘要 · Abstract (English)
Facial expressions are crucial to human communication, offering insights into emotional states. This study examines how specific facial features influence emotion classification, using facial perturbations on the Fer2013 dataset. As expected, models trained on data with the removal of some important facial feature experienced up to an 85% accuracy drop when compared to baseline for emotions like happy and surprise. Surprisingly, for the emotion disgust, there seem to be slight improvement in accuracy for classifier after mask have been applied. Building on top of this observation, we applied a training scheme to mask out facial features during training, motivating our proposed Perturb Scheme. This scheme, with three phases-attention-based classification, pixel clustering, and feature-focused training, demonstrates improvements in classification accuracy. The experimental results obtained suggests there are some benefits to removing individual facial features in emotion recognition tasks.
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