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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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WRAP-fusing-dynamic-deep-learned-features-handcrafted-features-facial-expression-recognition-Tjahjadi-2019.pdf - Accepted Version - Requires a PDF viewer. Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0. Download (873Kb) | Preview |
Official URL: http://dx.doi.org/10.1016/j.jvcir.2019.102659
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 | ||||||||||||
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| 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 |
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| 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 | ||||||||||||
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| 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: |
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