用配对学习提升情绪识别模型在不平衡数据上的表现
Pairwise Discernment of AffectNet Expressions with ArcFace
- 采用弧边损失的迁移学习框架，利用面部验证预训练模型
- 在不平衡数据上，配对学习使情绪识别准确率显著提升
- 适合关注情绪识别与数据不平衡问题的研究者
本研究探索通过面部情绪识别（FER）让计算机理解人类情绪。采用基于ResNeXt和EfficientNet的迁移学习，并引入原用于面部验证任务的ArcFace模型，基于标注了情绪信息的AffectNet数据库进行训练。实验表明，同域迁移学习具有价值，但数据不平衡会阻碍情绪模式的学习；而采用配对学习方法能有效缓解类别不平衡问题，显著提升模型在FER任务中的性能。
原文摘要 · Abstract (English)
This study takes a preliminary step toward teaching computers to recognize human emotions through Facial Emotion Recognition (FER). Transfer learning is applied using ResNeXt, EfficientNet models, and an ArcFace model originally trained on the facial verification task, leveraging the AffectNet database, a collection of human face images annotated with corresponding emotions. The findings highlight the value of congruent domain transfer learning, the challenges posed by imbalanced datasets in learning facial emotion patterns, and the effectiveness of pairwise learning in addressing class imbalances to enhance model performance on the FER task.
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