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Occlusion-Robust Face Detection Using Shallow and Deep Proposal Based Faster R-CNN Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Real-World Facial Expression Recognition Using Metric Learning Method

Authors : Zhiwen Liu, Shan Li, Weihong Deng

Published in: Biometric Recognition

Publisher: Springer International Publishing

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Abstract

Real-world human facial expressions recognition has great value in Human-Computer Interaction. Currently facial expression recognition methods perform quite poor in real-world compared with in traditional laboratory conditions. A key factor is the lack of reliable large real-world facial expression database. In this paper, a large and reliable real-world facial expression database and a Modified Metric Learning Method based on NCM classifier (PR-NCMML) to regress the probability distribution of emotional labels will be introduced. According to experiments, the six-dimension emotion probability vector derived by PR-NCMML is closer to human perception, which leads to better accuracy than the state-of-the-art methods, such as the SVM based algorithms, both dominant emotion prediction and multi-label emotion recognition.

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previous chapter Facial Expression Recognition Based on Ensemble of Mulitple CNNs
next chapter Recognizing Compound Emotional Expression in Real-World Using Metric Learning Method
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Metadata
Title
Real-World Facial Expression Recognition Using Metric Learning Method
Authors
Zhiwen Liu
Shan Li
Weihong Deng
Copyright Year
2016
Book
Biometric Recognition

Print ISBN: 978-3-319-46653-8

Electronic ISBN: 978-3-319-46654-5

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-46654-5_57

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