  This paper details the methodology and results of the EmotioNet challenge.
This challenge is the first to test the ability of computer vision algorithms
in the automatic analysis of a large number of images of facial expressions of
emotion in the wild. The challenge was divided into two tracks. The first track
tested the ability of current computer vision algorithms in the automatic
detection of action units (AUs). Specifically, we tested the detection of 11
AUs. The second track tested the algorithms' ability to recognize emotion
categories in images of facial expressions. Specifically, we tested the
recognition of 16 basic and compound emotion categories. The results of the
challenge suggest that current computer vision and machine learning algorithms
are unable to reliably solve these two tasks. The limitations of current
algorithms are more apparent when trying to recognize emotion. We also show
that current algorithms are not affected by mild resolution changes, small
occluders, gender or age, but that 3D pose is a major limiting factor on
performance. We provide an in-depth discussion of the points that need special
attention moving forward.
