Distribution-based iterative pairwise classification of emotions in the wild using LGBP-TOP

Automatic facial expression analysis promises to be a game-changer in many application areas. But before this promise can be fulfilled, it has to move from the laboratory into the wild. The Emotion Recognition in the Wild challenge provides an opportunity to develop approaches in this direction. We propose a novel Distribution-based Pairwise Iterative Classification scheme, which outperforms standard multi-class classification on this challenge data. We also verify that the recently proposed dynamic appearance descriptor, Local Gabor Patterns on Three Orthogonal Planes, performs well on this real-world data, indicating that it is robust to the type of facial misalignments that can be expected in such scenarios. Finally, we provide details of ACTC, our affective computing tools on the cloud, which is a new resource for researchers in the field of affective computing.