Evaluating Input Variable Effects of an Artificial Neural Network Modeling Facial Attractiveness

Artificial Neural Networks (ANNs) are powerful predictors, however, they essentially function like \u27black boxes\u27 because they lack explanatory power. Various algorithms have been developed to examine input influences and interactions thus enhancing understanding of the function being modeled. The study of facial attractiveness is one domain that could potentially benefit from ANN models. The literature shows that the relationship between attractiveness and facial attributes is complex and not yet fully understood. In this project, a feed-forward ANN was trained with backpropagation to 0.86 classification using 8-fold cross validation. The dataset consisted of 88 female facial images, each containing 17 geofacial measurements, a random noise variable, and a rating. Input \u27clamping\u27 and the Connection Weight Approach (Olden & Jackson, 2002), were implemented and the results were examined in terms of the facial attractiveness domain. In general, the results suggest that more feminized and asymmetrical features enhance facial attractiveness