Title :
Automatic Motion Feature Extraction with Application to Quantitative Assessment of Facial Paralysis
Author :
Shu He ; Soraghan, John J. ; O´Reilly, B.F.
Author_Institution :
Strathclyde Univ., Glasgow, UK
Abstract :
This paper presents a robust, objective, automated and quantitative assessment system for facial paralysis using artificial intelligence analysis of biomedical 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).
Keywords :
artificial intelligence; feature extraction; image motion analysis; mean square error methods; medical image processing; radial basis function networks; House-Brackmann scale; MSE; RBF neural network; artificial intelligence analysis; automatic motion feature extraction; biomedical video data; facial feature localization; facial movements detection; facial paralysis; mean squared error; optical flow; quantitative assessment; radial basis function neural network; Artificial intelligence; Artificial neural networks; Biomedical optical imaging; Face detection; Facial features; Feature extraction; Image motion analysis; Optical computing; Robustness; Testing; Facial Paralysis Measurement; House-Brackmann Scale; Optical Flow; RBF Neural Network;
Conference_Titel :
Acoustics, Speech and Signal Processing, 2007. ICASSP 2007. IEEE International Conference on
Conference_Location :
Honolulu, HI
Print_ISBN :
1-4244-0727-3
DOI :
10.1109/ICASSP.2007.366711
