Multimodal(Audio, Facial and Gesture) based Emotion Recognition challenge

Abstract

The emotion recognition in the wild has been a hot research topic in the field of a affective computing. Though some progresses have been achieved, the emotion recognition in the wild is still an unsolved problem due to the challenge of head movement, face deformation, illumination variation etc. To deal with these unconstrained challenges, we expand the focus to several expression forms to facilitate research on emotion recognition in the wild. The state-of-the-art CNN based object recognition models are employed to facilitate the facial expression recognition performance such as FAN[1],TSM[2]. The best experimental result shows that the overall accuracy of our algorithm on the validation dataset of the challenge is 76.43%.