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

EmotioNet Challenge: Recognition of facial expressions of emotion in the wild