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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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:	Date Event December 2019 Published 23 September 2019 Available 23 September 2019 Accepted
Date of first compliant deposit:	6 November 2019
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 ID RIOXX Funder Name Funder ID 61902187 [NSFC] National Natural Science Foundation of China http://dx.doi.org/10.13039/501100001809 UNSPECIFIED Innovative Team of Jiangsu Province http://dx.doi.org/10.13039/501100005065 UNSPECIFIED Doctorate Fellowship Foundation of Nanjing Forestry University http://dx.doi.org/10.13039/501100010019

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