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.
|Publication status||Published - Dec 2009|
|Event||1st International Conferfence on Computer Science from Algorithms to Applications (CSAA-2009) - Cairo, Egypt|
Duration: 8 Jan 2009 → 10 Jan 2009
|Conference||1st International Conferfence on Computer Science from Algorithms to Applications (CSAA-2009)|
|Period||8/01/09 → 10/01/09|
- facial paralysis measurement
- house-brackmann scale
- optical flow
- rbf neural network
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John Soraghan (Participant)
Impact: Impact - for External Portal › Health and welfare - new products, guidelines and services