  This paper addresses the question of emotion classification. The task
consists in predicting emotion labels (taken among a set of possible labels)
best describing the emotions contained in short video clips. Building on a
standard framework -- lying in describing videos by audio and visual features
used by a supervised classifier to infer the labels -- this paper investigates
several novel directions. First of all, improved face descriptors based on 2D
and 3D Convo-lutional Neural Networks are proposed. Second, the paper explores
several fusion methods, temporal and multimodal, including a novel hierarchical
method combining features and scores. In addition, we carefully reviewed the
different stages of the pipeline and designed a CNN architecture adapted to the
task; this is important as the size of the training set is small compared to
the difficulty of the problem, making generalization difficult. The so-obtained
model ranked 4th at the 2017 Emotion in the Wild challenge with the accuracy of
58.8 %.
