  Feature extraction plays a significant part in computer vision tasks. In this
paper, we propose a method which transfers rich deep features from a pretrained
model on face verification task and feeds the features into Bayesian ridge
regression algorithm for facial beauty prediction. We leverage the deep neural
networks that extracts more abstract features from stacked layers. Through
simple but effective feature fusion strategy, our method achieves improved or
comparable performance on SCUT-FBP dataset and ECCV HotOrNot dataset. Our
experiments demonstrate the effectiveness of the proposed method and clarify
the inner interpretability of facial beauty perception.
