Abstract
Face palsy has adverse effects on the appearance of a person and has negative social and functional consequences on the patient. Deep learning methods can improve face palsy detection rate, but their efficiency is limited by insufficient data, class imbalance, and high misclassification rate. To alleviate the lack of data and improve the performance of deep learning models for palsy face detection, data augmentation methods can be used. In this paper, we propose a novel Voronoi decomposition-based random region erasing (VDRRE) image augmentation method consisting of partitioning images into randomly defined Voronoi cells as an alternative to rectangular based random erasing method. The proposed method augments the image dataset with new images, which are used to train the deep neural network. We achieved an accuracy of 99.34% using two-shot learning with VDRRE augmentation on palsy faces from Youtube Face Palsy (YFP) dataset, while normal faces are taken from Caltech Face Database. Our model shows an improvement over state-of-the-art methods in the detection of facial palsy from a small dataset of face images.
Highlights
Facial palsy, commonly referred to as Bell’s palsy, is a major kind of facial nerve paralysis that leads to the loss of control of muscles in the affected facial areas [1]
This paper introduced a classification workflow based on deep learning for facial palsy detection and classification
The proposed methodology can be applied for facial palsy assessment using various facial palsy datasets, including the multi-class ones, which have face images labeled with palsy severity grades
Summary
Commonly referred to as Bell’s palsy, is a major kind of facial nerve paralysis that leads to the loss of control of muscles in the affected facial areas [1]. Some of the symptoms include the deformity of the face and dysfunction of facial expressions on the affected side of the face. The impact of the disease in face palsy-affected patients could lead to serious disruption to their everyday living. The detection of facial palsy depends solely on expert clinicians by performing a visual examination of facial symmetry and evaluation of facial expression dysfunction. The major challenge in the diagnosis of facial palsy is the lack of successful measures targeted towards the effective evaluation of facial nerve function, as it could play a crucial role in understanding the advancement of the disease [3]
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