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2018 | OriginalPaper | Chapter

Improving Ensemble Learning Performance with Complementary Neural Networks for Facial Expression Recognition

Authors : Xinmin Zhang, Yingdong Ma

Published in: Artificial Neural Networks and Machine Learning – ICANN 2018

Publisher: Springer International Publishing

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Abstract

Facial expression recognition has significant application value in fields such as human-computer interaction. Recently, Convolutional Neural Networks (CNNs) have been widely utilized for feature extraction and expression recognition. Network ensemble is an important step to improve recognition performance. To improve the inefficiency of existing ensemble strategy, we propose a new ensemble method to efficiently find networks with complementary capabilities. The proposed method is verified on two groups of CNNs with different depth (eight 5-layer shallow CNNs and twelve 11-layer deep VGGNet variants) trained on FER-2013 and RAF-DB, respectively. Experimental results demonstrate that the proposed method achieves the highest recognition accuracy of 74.14% and 85.46% on FER-2013 and RAF-DB database, respectively, to the best of our knowledge, outperforms state-of-the-art CNN-based facial expression recognition methods. In addition, our method also obtains a competitive result of the mean diagonal value in confusion matrix on RAF-DB test set.

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Metadata
Title
Improving Ensemble Learning Performance with Complementary Neural Networks for Facial Expression Recognition
Authors
Xinmin Zhang
Yingdong Ma
Copyright Year
2018
Book
Artificial Neural Networks and Machine Learning – ICANN 2018

Print ISBN: 978-3-030-01417-9

Electronic ISBN: 978-3-030-01418-6

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-030-01418-6_73

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