# Transferring Rich Deep Features for Facial Beauty Prediction

arXiv: https://arxiv.org/abs/1803.07253 · HTML: https://mlsanity.com/paper/1803.07253/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Mar 2018.

## Built on by (most cited first, 5 of 5)

- [Lightweight Facial Attractiveness Prediction Using Dual Label Distribution](https://mlsanity.com/paper/2212.01742/) (2022)
- [Learning to Select Visual In-Context Demonstrations](https://mlsanity.com/paper/2603.26775/) (2026)
- [PETS-SWINF: A regression method that considers images with metadata based Neural Network for pawpularity prediction on 2021 Kaggle Competition "PetFinder.my"](https://mlsanity.com/paper/2201.06061/) (2022)
- [Detection of Digital Facial Retouching utilizing Face Beauty Information](https://mlsanity.com/paper/2512.08397/) (2025)
- [Facial beauty prediction fusing transfer learning and broad learning system](https://mlsanity.com/paper/2603.16930/) (2026)

## Abstract (arXiv)

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.