EmoNets: Multimodal deep learning approaches for emotion recognition in video
Abstract
The task of the emotion recognition in the wild (EmotiW) Challenge is to assign one of seven emotions to short video clips extracted from Hollywood style movies. The videos depict acted-out emotions under realistic conditions with a large degree of variation in attributes such as pose and illumination, making it worthwhile to explore approaches which consider combinations of features from multiple modalities for label assignment. In this paper we present our approach to learning several specialist models using deep learning techniques, each focusing on one modality. Among these are a convolutional neural network, focusing on capturing visual information in detected faces, a deep belief net focusing on the representation of the audio stream, a K-Means based "bag-of-mouths" model, which extracts visual features around the mouth region and a relational autoencoder, which addresses spatio-temporal aspects of videos. We explore multiple methods for the combination of cues from these modalities into one common classifier. This achieves a considerably greater accuracy than predictions from our strongest single-modality classifier. Our method was the winning submission in the 2013 EmotiW challenge and achieved a test set accuracy of 47.67% on the 2014 dataset.
Cite
@article{arxiv.1503.01800,
title = {EmoNets: Multimodal deep learning approaches for emotion recognition in video},
author = {Samira Ebrahimi Kahou and Xavier Bouthillier and Pascal Lamblin and Caglar Gulcehre and Vincent Michalski and Kishore Konda and Sébastien Jean and Pierre Froumenty and Yann Dauphin and Nicolas Boulanger-Lewandowski and Raul Chandias Ferrari and Mehdi Mirza and David Warde-Farley and Aaron Courville and Pascal Vincent and Roland Memisevic and Christopher Pal and Yoshua Bengio},
journal= {arXiv preprint arXiv:1503.01800},
year = {2015}
}