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How Deep Neural Networks Can Improve Emotion Recognition on Video Data

By Pooya Khorrami, Tom Le Paine, Kevin Brady, Charlie Dagli and Thomas S. Huang

Abstract

We consider the task of dimensional emotion recognition on video data using deep learning. While several previous methods have shown the benefits of training temporal neural network models such as recurrent neural networks (RNNs) on hand-crafted features, few works have considered combining convolutional neural networks (CNNs) with RNNs. In this work, we present a system that performs emotion recognition on video data using both CNNs and RNNs, and we also analyze how much each neural network component contributes to the system's overall performance. We present our findings on videos from the Audio/Visual+Emotion Challenge (AV+EC2015). In our experiments, we analyze the effects of several hyperparameters on overall performance while also achieving superior performance to the baseline and other competing methods.Comment: Accepted at ICIP 2016. Fixed typo in Experiments sectio

Topics: Computer Science - Computer Vision and Pattern Recognition
Year: 2017
OAI identifier: oai:arXiv.org:1602.07377

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