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2018 | OriginalPaper | Chapter

Emotion Recognition Based on Gramian Encoding Visualization

Authors : Jie-Lin Qiu, Xin-Yi Qiu, Kai Hu

Published in: Brain Informatics

Publisher: Springer International Publishing

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Abstract

This paper addresses the problem that emotional computing is difficult to be put into real practical fields intuitively, such as medical disease diagnosis and so on, due to poor direct understanding of physiological signals. In view of the fact that people’s ability to understand two-dimensional images is much higher than one-dimensional signals, we use Gramian Angular Fields to visualize time series signals. GAF images are represented as a Gramian matrix where each element is the trigonometric sum between different time intervals. Then we use Tiled Convolutional Neural Networks (tiled CNNs) on 3 real world datasets to learn high-level features from GAF images. The classification results of our method are better than the state-of-the-art approaches. This method makes visualization based emotion recognition become possible, which is beneficial in the real medical fields, such as making cognitive disease diagnosis more intuitively.

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next chapter EEG Based Brain Mapping by Using Frequency-Spatio-Temporal Constraints
Footnotes
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Metadata
Title
Emotion Recognition Based on Gramian Encoding Visualization
Authors
Jie-Lin Qiu
Xin-Yi Qiu
Kai Hu
Copyright Year
2018
Book
Brain Informatics

Print ISBN: 978-3-030-05586-8

Electronic ISBN: 978-3-030-05587-5

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-030-05587-5_1

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