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Fusing dynamic deep learned features and handcrafted features for facial expression recognition

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Fan, Xijian and Tjahjadi, Tardi (2019) Fusing dynamic deep learned features and handcrafted features for facial expression recognition. Journal of Visual Communication and Image Representation, 65 . 102659. doi:10.1016/j.jvcir.2019.102659

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Official URL: http://dx.doi.org/10.1016/j.jvcir.2019.102659

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Abstract

The automated recognition of facial expressions has been actively researched due to its wide-ranging applications. The recent advances in deep learning have improved the performance facial expression recognition (FER) methods. In this paper, we propose a framework that combines discriminative features learned using convolutional neural networks and handcrafted features that include shape- and appearance-based features to further improve the robustness and accuracy of FER. In addition, texture information is extracted from facial patches to enhance the discriminative power of the extracted textures. By encoding shape, appearance, and deep dynamic information, the proposed framework provides high performance and outperforms state-of-the-art FER methods on the CK+ dataset.

Item Type: Journal Article
Subjects: Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
T Technology > TA Engineering (General). Civil engineering (General)
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Science > Engineering
Library of Congress Subject Headings (LCSH): Neural networks (Computer science) , Human face recognition (Computer science) , Signal processing -- Digital techniques, Pattern recognition systems, Computer vision
Journal or Publication Title: Journal of Visual Communication and Image Representation
Publisher: Academic Press Inc Elsevier Science
ISSN: 1047-3203
Official Date: December 2019
Dates:
DateEvent
December 2019Published
23 September 2019Available
23 September 2019Accepted
Volume: 65
Article Number: 102659
DOI: 10.1016/j.jvcir.2019.102659
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Restricted or Subscription Access
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
61902187[NSFC] National Natural Science Foundation of Chinahttp://dx.doi.org/10.13039/501100001809
UNSPECIFIEDInnovative Team of Jiangsu Provincehttp://dx.doi.org/10.13039/501100005065
UNSPECIFIEDDoctorate Fellowship Foundation of Nanjing Forestry Universityhttp://dx.doi.org/10.13039/501100010019

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