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3DPalsyNet: a facial palsy grading and motion recognition framework using fully 3D convolutional neural networks

Storey, Gary, Jiang, Richard, Keogh, Shelagh, Bouridane, Ahmed and Li, Chang-Tsun 2019, 3DPalsyNet: a facial palsy grading and motion recognition framework using fully 3D convolutional neural networks, IEEE access, vol. 7, pp. 121655-121664, doi: 10.1109/ACCESS.2019.2937285.

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Title 3DPalsyNet: a facial palsy grading and motion recognition framework using fully 3D convolutional neural networks
Author(s) Storey, Gary
Jiang, Richard
Keogh, Shelagh
Bouridane, Ahmed
Li, Chang-TsunORCID iD for Li, Chang-Tsun orcid.org/0000-0003-4735-6138
Journal name IEEE access
Volume number 7
Start page 121655
End page 121664
Total pages 10
Publisher IEEE
Place of publication PIscataway, N.J.
Publication date 2019
ISSN 2169-3536
Keyword(s) Science & Technology
Technology
Computer Science, Information Systems
Engineering, Electrical & Electronic
Telecommunications
Computer Science
Engineering
Computer vision
face detection
facial action recognition
machine learning
PATTERNS
Language eng
DOI 10.1109/ACCESS.2019.2937285
Indigenous content off
HERDC Research category C1 Refereed article in a scholarly journal
Grant ID EP/P009727/1
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30133267

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.