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Extraction and representation of common feature from uncertain facial expressions with cloud model

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Abstract

Human facial expressions are key ingredient to convert an individual’s innate emotion in communication. However, the variation of facial expressions affects the reliable identification of human emotions. In this paper, we present a cloud model to extract facial features for representing human emotion. First, the uncertainties in facial expression are analyzed in the context of cloud model. The feature extraction and representation algorithm is established under cloud generators. With forward cloud generator, facial expression images can be re-generated as many as we like for visually representing the extracted three features, and each feature shows different roles. The effectiveness of the computing model is tested on Japanese Female Facial Expression database. Three common features are extracted from seven facial expression images. Finally, the paper is concluded and remarked.

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Acknowledgments

The authors would like to thank Professor Kevin P. Chen and Ching-Chung Li for their proof-reading and comments for this paper. This work was supported in part by a grant from National Natural Science Fund of China (61472039), National Key Research and Development Plan of China (2016YFC0803000, 2016YFB0502604), and Frontier and interdisciplinary innovation program of Beijing Institute of Technology (2016CX11006).

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Authors and Affiliations

  1. School of Software, Beijing Institute of Technology, 5 South Zhongguancun Street, Haidian District, Beijing, 100081, China

    Shuliang Wang, Hanning Yuan & Jing Geng

  2. State Key Laboratory of Software Engineering, Wuhan University, Wuchang, Wuhan, 430072, China

    Hehua Chi

Authors
  1. Shuliang Wang
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  2. Hehua Chi
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  3. Hanning Yuan
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  4. Jing Geng
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Corresponding author

Correspondence to Hanning Yuan.

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Responsible editor: Philippe Garrigues

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Wang, S., Chi, H., Yuan, H. et al. Extraction and representation of common feature from uncertain facial expressions with cloud model. Environ Sci Pollut Res 24, 27778–27787 (2017). https://doi.org/10.1007/s11356-017-0237-2

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  • DOI: https://doi.org/10.1007/s11356-017-0237-2

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