Paper Info
Title
Automatic Grading of Palsy Using Asymmetrical Facial Features: A Study Complemented by New Solutions.
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.
Year
DOI
Venue
2018
10.3390/sym10070242
SYMMETRY-BASEL
Keywords
Field
DocType
convolutional neural networks,facial palsy grading,datasets,generative adversial networks,primary health care
Network on,Primary health care,Combinatorics,Palsy,Grading (education),Pattern recognition,Convolutional neural network,Facial symmetry,Artificial intelligence,Overfitting,Discriminative model,Mathematics
Journal
Volume
Issue
Citations 
10
7.0
2
PageRank 
References 
Authors
0.41
11
6
Name
Order
Citations
PageRank
Muhammad Sajid171.91
Tamoor Shafique241.16
Mirza Jabbar Aziz Baig320.75
Imran Riaz441.16
Shahid Amin520.41
Sohaib Manzoor672.60