NEURAL NETWORK COMPUTABILITY OF FACE-BASED ATTRACTIVENESS

In this work we have explored facial attractiveness as well as sex classification through the application of feed-forward artificial neural network (ANN) models. Data was collected from participants to compile a face database that was later rated by human raters. The neural network analyzed facial images as pixel-data that was converted into vectors. Prediction was carried out by first training the neural network on a number of images (along with their respective attractiveness ratings) and then testing it on new stimuli in order to make generalizations. There was strong intraclass correlation (ICC) and agreement between the neural network outputs and the human raters on facial attractiveness. This project's success provides novel evidence for the hypothesis that there are objective regularities in facial attractiveness. In addition, there is some indication that the confidence with which sex classification is performed is related to attractiveness. This paper corroborates the work of others that suggests facial attractiveness judgments can be learned by machines.