Despite much progress in the field of facial expression recognition, little attention has been paid to the recognition of peak emotion. Aviezer et al. [1] showed that humans have trouble discerning between positive and negative peak emotions. In this work we analyze how deep learning fares on this challenge. We find that (i) despite using very small datasets, features extracted from deep learning models can achieve results significantly better than humans. (ii) We find that deep learning models, even when trained only on datasets tagged by humans, still outperform humans in this task.
翻译:尽管在面部表情识别领域取得了很大进展,但人们很少注意对高峰情绪的认知。 Aviezer等人[1] 显示,人类难以辨别正负高峰情绪。在这项工作中,我们分析了在这一挑战上的深层学习效果。我们发现,(一) 尽管使用非常小的数据集,从深层学习模型中提取的特征能够比人类取得显著更好的成果。 (二) 我们发现,深层学习模式,即使仅接受人类标记的数据集培训,在这项工作中仍然比人类表现更好。