Deep Facial Features for Personalized Attractiveness Prediction

In this work, we propose a novel personalized facial attractiveness prediction method that is able to effectively learn an individual's preferences on few training images. A deep convolutional neural network (CNN) was first employed to estimate facial attributes. Then the attributes that play the most significant role for the individual were selected to train Random Forest. A new dataset specially created for personalized beauty evaluation was also proposed. Our method has achieved promising results of 54% Pearson's Correlation on 15 training images, 61% on 35 images.