Journal of Data Science

Abstract: 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.

Probabilistic topic models have become a standard in modern machine learning to deal with a wide range of applications. Representing data by dimensional reduction of mixture proportion extracted from topic models is not only richer in semantics interpretation, but could also be informative for classification tasks. In this paper, we describe the Topic Model Kernel (TMK), a topicbased kernel for Support Vector Machine classification on data being processed by probabilistic topic models. The applicability of our proposed kernel is demonstrated in several classification tasks with real world datasets. TMK outperforms existing kernels on the distributional features and give comparative results on nonprobabilistic data types.