Abstract:
Research in automatic affect recognition has come a long way. This
paper describes the fifth Emotion Recognition in the Wild (EmotiW)
challenge 2017. EmotiW aims at providing a common benchmarking
platform for researchers working on different aspects of affective
computing. This year there are two sub-challenges: a) Audio-video
emotion recognition and b) group-level emotion recognition. These
challenges are based on the acted facial expressions in the wild
and group affect databases, respectively. The particular focus of
the challenge is to evaluate method in ‘in the wild’ settings. ‘In the
wild’ here is used to describe the various environments represented
in the images and videos, which represent real-world (not lab like)
scenarios. The baseline, data, protocol of the two challenges and
the challenge participation are discussed in detail in this paper.
