Comparative beauty classification for pre-surgery planning

Recent medical studies show that there exist aesthetic ideal features for facial beauty based on facial proportions. Automated tools that can provide information about the prediction of how the surgery will improve the patients' perceived beauty or 'peer-esteem' will find applications in various areas. In our previous work, we introduced an automated procedure based on image analysis and supervised learning that confirmed the existence of general rules in peer-esteem measurement. In this paper, we further experimented our automated system by extending the analysis of classification tools and human data by comparing a number of classifiers, namely Decision Trees, Multi-Layer Perceptron and Kernel Density Estimators. Results are good since the standardized distance is generally less than one class, and prove that these classifiers can be used to reliably predict the consensus of a large and varied population of human referees, hence providing peer-esteem information for patients.