Review of automated emotion-based quantification of facial expression in Parkinson’s patients

Abstract

Among various means of communication, the human face is utmost powerful. Persons suffering from Parkinson’s disease (PD) experience hypomimia which often leads to reduction in facial expression. Hypomimia affects in social interaction and has a highly undesirable impact on patient’s as well as his relative’s quality of life. To track the longitudinal progression of PD, usually Movement Disorder Society’s Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) is used in clinical studies and item 3.2 (i.e., facial expression) of MDS-UPDRS defines hypomimia levels. Assessment of facial expressions has traditionally relied on an observer-based scale which can be time-consuming. Computational analysis techniques for facial expressions can assist the clinician in decision making. Intention of such techniques is to predict objective and accurate score for facial expression. The aim of this paper is to present up-to-date review on computational analysis techniques for measurement of emotional facial expression of people with PD (PWP) along with an overview on clinical applications of automated facial expression analysis. This led us to examine a pilot experimental work for masked face detection in PD. For the same, a deep learning-based model was trained on NVIDIA GeForce 920M GPU. It was observed that deep learning-based model yields 85% accuracy on the testing images.