Facial Paralysis Recognition Using Face Mesh-Based Learning

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 learning-based 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.