Temporal Multimodal Fusion for Video Emotion Classification in the Wild
This work addresses emotion recognition in videos for applications like human-computer interaction, but it is incremental as it builds on standard multimodal frameworks.
The paper tackled video emotion classification by proposing improved face descriptors and novel fusion methods, achieving 58.8% accuracy and ranking 4th in the 2017 Emotion in the Wild challenge.
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 %.