autoFPR: An Efficient Automatic Approach for Facial Paralysis Recognition Using Facial Features

Facial paralysis (FP) is the most common illness. Nerve damage can cause the affected muscles of the face to lose control. Most FP diagnosis systems heavily depend on skilled clinicians and lack automatic quantitative assessment. This paper introduces a novel automatic facial paralysis recognition (autoFPR) approach, a four-stage machine learning solution, for classifying FP and healthy subjects. Our solution includes the creation of the dataset, feature extraction, dimensionality reduction, and classification. The FP and healthy subject videos are collected from the publicly available YouTube Facial Palsy database and the 300VW database, respectively. Facial features are extracted from the collected facial videos using landmarks, action units, eye gaze, and head pose. Five different experiments were performed to find the best facial paralysis recognition technique. autoFPR can distinguish subjects with facial paralysis and healthy individuals by using movement changes in their eyebrows, eyes, and corners of the mouth. Our goal is to prove that machine learning classifiers are capable of diagnosing FP. We evaluate system performance using accuracy, precision, recall, and F1. Feature selection plus principal component analysis with support vector machine achieved the highest accuracy, with 97.3% for the extracted features.