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

He, Shu and Soraghan, John and O'Reilly, Brian F (2007) Automatic motion feature extraction with application to quantitative assessment of facial paralysis. In: 2007 IEEE International conference on acoustics, speech and signal processing, Volume I, Parts 1-3, Proceedings. International Conference on Acoustics Speech and Signal Processing (ICASSP) . IEEE, New York, pp. 441-444. ISBN 9781424407286

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Abstract

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).