Gaussian hamming distance: De-identified features of facial expressions

Abstract

We present new image features for diagnosing general wellbeing states and medical conditions. The new method, called Gaussian Hamming Distance (GHD), generates de-identified features that are highly correlated with general wellbeing states, such as happiness, smoking, and facial palsy. This method allows aid organizations and governments in developing countries to provide affordable medical services. We evaluate the new approach using real face-image data and four classifiers: Naive Bayesian classier, Artificial Neural Network, Decision Tree, and Support Vector Machines (SVM) for predicting general wellbeing states. Its predictive power (over 93 % accuracy) is suitable for providing a variety of online services including recommending useful health information for improving general wellbeing states.