Laser & Optoelectronics Progress, Volume. 55, Issue 1, 11002(2018)

Facial Expression Recognition Based on Fusion of Local Features and Deep Belief Network

Wang Linlin1、*, Liu Jinghao1, and Fu Xiaomei2
Author Affiliations
  • 1School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
  • 2School of Marine Science and Technology, Tianjin University, Tianjin 300072, China
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    References(31)

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    [14] Li Y, Mavadati S M, Mahoor M H. et al. A unified probabilistic framework for measuring the intensity of spontaneous facial action units[C]. IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, 1-7(2013).

    [15] Wang Q, Jia K, Liu P. Design and implementation of remote facial expression recognition surveillance system based on PCA and KNN algorithms[C]. International Conference on Intelligent Information Hiding and Multimedia Signal Processing., 314-317(2016).

    [19] Lv Y, Feng Z, Xu C. Facial expression recognition via deep learning[C]. International Conference on Smart Computing, IEEE, 303-308(2015).

    [20] Liu P, Han S, Meng Z et al. Facial expression recognition via a boosted deep belief network[C]. IEEE Conference on Computer Vision and Pattern Recognition, 1805-1812(2014).

    [24] Happy S L, Routray A. Robust facial expression classification using shape and appearance features[C]. Eighth International Conference on Advances in Pattern Recognition, 1-5(2015).

    [31] Ghimire D, Jeong S, Yoon S et al. Facial expression recognition based on region specific appearance and geometric features[C]. Tenth International Conference on Digital Information Management, IEEE, 142-147(2016).

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    Wang Linlin, Liu Jinghao, Fu Xiaomei. Facial Expression Recognition Based on Fusion of Local Features and Deep Belief Network[J]. Laser & Optoelectronics Progress, 2018, 55(1): 11002

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    Paper Information

    Category: Image Processing

    Received: May. 27, 2017

    Accepted: --

    Published Online: Sep. 10, 2018

    The Author Email: Wang Linlin (wanglinlin@tju.edu.cn)

    DOI:10.3788/LOP55.011002

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