Neural Networks for Emotion Recognition in the Wild

Abstract

In this paper we present neural networks based method for emotion recognition. Proposed model was developed as part of 2014 Emotion Recognition in the Wild Challenge. It is composed of modality specific neural networks, which where trained separately on audio and video data extracted from short video clips taken from various movies. Each network was trained on frame-level data, which in later stages were aggregated by simple averaging of predicted class distributions for each clip. In the next stage various techniques for combining modalities where investigated with the best being support vector machine with RBF kernel. Our method achieved accuracy of 37.84%, which is better than 33.7% obtained by the best baseline model provided by organisers.