Facial Paresis Index Prediction by Exploiting Active Appearance Models for Compact Discriminative Features

In the field of otorhinolaryngology, the dysfunction of the facial nerve is a common disease which results in a paresis of usually one half of the patients face. The grade of paralysis is measured by physicians with rating scales, e.g. the Stennert Index or the House-Brackmann scale. In this work, we propose a method to analyse and predict the severity of facial paresis on the basis of single images. We combine feature extraction methods based on a generative approach (Active Appearance Models) with a fast non-linear classifier (Random Decision Forests) in order to predict the patients grade of facial paresis. In our proposed framework, we make use of highly discriminative features based on the fitting parameters of the Active Appearance Model, Action Units and Landmark distances. We show in our experiments that it is possible to correctly predict the grade of facial paresis in many cases, although the visual appearance is strongly varying. The presented method creates new opportunities to objectively document the patients progress in therapy.