  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.
