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Symmetry 2018, 10(7), 242; https://doi.org/10.3390/sym10070242

Automatic Grading of Palsy Using Asymmetrical Facial Features: A Study Complemented by New Solutions

1
Department of Electrical Engineering, Mirpur University of Science and Technology (MUST), Mirpur 10250 (AJK), Pakistan
2
Faculty of Computing, Engineering and Science, Staffordshire University, Stoke-on-Trent ST4 2DE, UK
3
Department of Electrical Engineering (Power), Mirpur University of Science and Technology, Mirpur 10250 (AJK), Pakistan
4
School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China
*
Author to whom correspondence should be addressed.
Received: 29 May 2018 / Revised: 19 June 2018 / Accepted: 25 June 2018 / Published: 26 June 2018
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Abstract

Facial palsy caused by nerve damage results in loss of facial symmetry and expression. A reliable palsy grading system for large-scale applications is still missing in the literature. Although numerous approaches have been reported on facial palsy quantification and grading, most employ hand-crafted features on relatively smaller datasets which limit the classification accuracy due to non-optimal face representation. In contrast, convolutional neural networks (CNNs) automatically learn the discriminative features facilitating the accurate classification of underlying tasks. In this paper, we propose to apply a typical deep network on a large dataset to extract palsy-specific features from face images. To prevent the inherent limitation of overfitting frequently occurring in CNNs, a generative adversial network (GAN) is applied to augment the training dataset. The deeply learned features are then used to classify the palsy disease into five benchmarked grades. The experimental results show that the proposed approach offers superior palsy grading performance compared to some existing methods. Such an approach is useful for palsy grading at large scale, such as primary health care. View Full-Text
Keywords: convolutional neural networks; facial palsy grading; datasets; generative adversial networks; primary health care convolutional neural networks; facial palsy grading; datasets; generative adversial networks; primary health care
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Sajid, M.; Shafique, T.; Baig, M.J.A.; Riaz, I.; Amin, S.; Manzoor, S. Automatic Grading of Palsy Using Asymmetrical Facial Features: A Study Complemented by New Solutions. Symmetry 2018, 10, 242.

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