  Central palsy is a form of facial paralysis that requires urgent medical
attention and has to be differentiated from other, similar conditions such as
peripheral palsy. To aid in fast and accurate diagnosis of this condition, we
propose a machine learning approach to automatically classify peripheral and
central facial palsy. The Palda dataset is used, which contains 103 peripheral
palsy images, 40 central palsy, and 60 healthy people. Experiments are run on
five machine learning algorithms. The best performing algorithms were found to
be the SVM (total accuracy of 85.1%) and the Gaussian naive Bayes (80.7%). The
lowest false negative rate on central palsy was achieved by the naive Bayes
approach (80% compared to 70%). This condition could prove to be the most
severe, and thus its sensitivity is another good way to compare algorithms. By
extrapolation, a dataset size of 334 total pictures is estimated to achieve a
central palsy sensitivity of 95%. All code used for these machine learning
experiments is freely available online at https://github.com/cvvletter/palsy.
