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2016 | OriginalPaper | Buchkapitel

Real-World Facial Expression Recognition Using Metric Learning Method

verfasst von : Zhiwen Liu, Shan Li, Weihong Deng

Erschienen in: Biometric Recognition

Verlag: 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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Metadaten
Titel
Real-World Facial Expression Recognition Using Metric Learning Method
verfasst von
Zhiwen Liu
Shan Li
Weihong Deng
Copyright-Jahr
2016
DOI
https://doi.org/10.1007/978-3-319-46654-5_57