Authors:
Zeerak Baig
and
Dustin van der Haar
Affiliation:
Academy of Computer Science and Software Engineering, University of Johannesburg, Kingsway Avenue and University Rd, Auckland Park, South Africa
Keyword(s):
Facial Paralysis, Machine Learning, Support Vector Machine, XGBoost, K Nearest Neighbour, CNN, MobileNetV2, Face Mesh.
Abstract:
Facial paralysis is a medical disorder caused by a compressed or enlarged seventh cranial nerve. The facial
muscles become weak or paralysed because of the compression. Many medical experts believe that viral
infection is the most common cause of facial paralysis; however, the origin of nerve injury is unknown. Facial
paralysis hampers a patient’s ability to blink, swallow, or communicate. This article proposes deep learningbased and traditional machine learning-based approaches for facial paralysis recognition in facial images,
which can aid in developing standardised medical evaluation tools. The proposed method first detects faces or
faces in each image, then extracts a face mesh from the given image using Google’s Mediapipe. The face mesh
descriptors are then transformed into a novel face mesh image, fed into the final component, comprised of a
convolutional neural network (CNN) to perform overall predictions. The study uses YouTube facial paralysis
datasets (Youtube and
Stroke face) and control datasets (CK+ and TUFTS face) to train and test the model
for unhealthy patients. The best approach achieved an accuracy of 98.93% with a MobilenetV2 backbone
using the YouTube facial paralysis dataset and the Stroke face dataset for palsy images, thereby showing mesh
learning can be accomplished using a CNN.
(More)