Support Vector Machines for Classification of Temporomandibular Disorders from Facial Pattern Values

10.6339/JDS.2011.09(3).935

Mansoureh Ghodsi；Saeid Sanei

Temporomandibular disorder ； maximum Lyapunov exponents ； support vector machine

Journal of Data Science

9卷3期（2011 / 07 / 01）

373 - 388

英文

The aim of this study is to develop a method for detection of temporomandibular disorder (TMD) based on visual analysis of facial movements. We analyse the motion of colour markers placed on the locations of interest on subjects faces in the video frames. We measured several features from motion patterns of the markers that can be used to distinguish between different classes. In our approach, both static and dynamic features are measured from a number of time sequences for classification of the subjects. A measure of nonlinear dynamics of the variations in the movement of colour markers positioned on the subjects faces was obtained via estimating the maximum Lyapunov exponent. Static features such as the number of outliers and kurtosis have also been evaluated. Then, Support Vector Machines (SVMs) are used to automatically classify all the subjects as belonging to individuals with TMD and healthy subjects.