Abstract
Facial paralysis is a condition causing decreased movement on one side of the face. A quantitative, objective, and reliable assessment system would be an invaluable tool for clinicians treating patients with this condition. This paper presents an approach based on the automatic analysis of patient video data. Facial feature localization and facial movement detection methods are discussed. An algorithm is presented to process the optical flow data to obtain the motion features in the relevant facial regions. Three classification methods are applied to provide quantitative evaluations of regional facial nerve function and the overall facial nerve function based on the House-Brackmann scale. Experiments show the radial basis function (RBF) neural network to have superior performance.
Highlights
Facial paralysis is a condition where damage to the facial nerve causes weakness of the muscles on one side of the face resulting in an inability to close the eye and dropping of the angle of the mouth
The results show that the radial basis function (RBF) NN outperforms the k-nearest neighbor (k-NN) and surport vector machine (SVM)
The results show that the best agreement is in the forehead region as in this region the optical flow can be estimated with a high degree of accuracy
Summary
Facial paralysis is a condition where damage to the facial nerve causes weakness of the muscles on one side of the face resulting in an inability to close the eye and dropping of the angle of the mouth. The commonest cause of facial palsy is a presumed herpes simplex viral infection, commonly referred to as Bell’s palsy, which causes temporary damage to the facial nerve. Treatment of such viral infections has been the source of controversy in the past, partly because it has been difficult to audit the effectiveness of treatment. As the facial nerve is often damaged during the neurosurgical removal of these intracranial benign tumours of the hearing nerve, facial nerve function is a commonly used indicator of the degree of success of the surgical technique. As most methods of assessing facial function are subjective, there is a considerable variability in the results between different assessors
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