Automatic motion feature extraction with application to quantitative assessment of facial paralysis

This paper presents a robust, objective, automated and quantitative assessment system for Facial Paralysis using artificial intelligence analysis of biomedial video data. Facial feature localization and prescribed facial movements detection are discussed. Optical flow is used to obtain the motion features in the relevant facial regions. Radial Basis Function (RBF) Neural Network is applied to provide quantitative evaluation of Facial Paralysis based on the House-Brackmann Scale. The results from 197 videos of 87 subjects are encouraging with a Mean Squared Error (MSE) of 0.013 (training) and 0.0169 (testing)