Why is facial expression analysis in the wild challenging?

In this paper, we discuss the challenges for facial expression analysis in the wild. We studied the problems exemplarily on the Emotion Recognition in the Wild Challenge 2013 [3] dataset. We performed extensive experiments on this dataset comparing different approaches for face alignment, face representation, and classification, as well as human performance. It turns out that under close-to-real conditions, especially with co-occurring speech, it is hard even for humans to assign emotion labels to clips when only taking video into account. Our experiments on automatic emotion classification achieved at best a correct classification rate of 29.81% on the test set using Gabor features and linear support vector machines, which were trained on web images. This result is 7.06% better than the official baseline, which additionally incorporates time information.