Publication Date:
2018-06-27
Description:
Symmetry, Vol. 10, Pages 242: Automatic Grading of Palsy Using Asymmetrical Facial Features: A Study Complemented by New Solutions Symmetry doi: 10.3390/sym10070242 Authors: Muhammad Sajid Tamoor Shafique Mirza Jabbar Aziz Baig Imran Riaz Shahid Amin Sohaib Manzoor 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.
Electronic ISSN:
2073-8994
Topics:
Mathematics
,
Physics
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