A Peek at Peak Emotion Recognition
This addresses a specific challenge in emotion recognition for applications like psychology or human-computer interaction, but it is incremental as it applies existing deep learning methods to a new problem.
The paper tackled the problem of recognizing peak emotions from facial expressions, where humans struggle to distinguish between positive and negative peaks, and found that deep learning models, even with small datasets or human-tagged training data, significantly outperform humans in this task.
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.