Application of Machine Learning for Facial Stroke Detection

Abstract

According to clinical reports, people with ages between 60 to 79 years have a high risk of stroke. The most obvious facial features of stroke are expressional asymmetry and mouth askew. In this study, we proposed a facial stroke recognition system that assists patients in self-judgment. Facial landmarks were tracked by an ensemble of regression trees (ERT) method. Two symmetry indexes area ratio and distance ratio between the left and right side of the eye and mouth were calculated. Local Ternary Pattern (LTP) and Gabor filter were used to enhance and to extract the texture features of the region of interest (ROI), respectively. The structural similarity of ROI between the left and right face was calculated. After that, we modified the original feature selection algorithm to select the best feature set. To classify facial stroke, the Support Vector Machine (SVM), Random Forest (RF), and Bayesian Classifier were adopted as classifier. The experimental results show that the proposed system can accurately and effectively distinguish stroke from facial images. The recognition accuracy of SVM, Random Forest, and Bayes are 100%, 95.45%, and 100%, respectively.