Transferring Rich Deep Features for Facial Beauty Prediction
This work addresses facial beauty prediction for computer vision applications, but it is incremental as it builds on existing methods with feature fusion.
The paper tackled facial beauty prediction by transferring deep features from a pretrained face verification model and using Bayesian ridge regression, achieving improved or comparable performance on the SCUT-FBP and ECCV HotOrNot datasets.
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.