This repository holds the PyTorch implementation for our paper CRNet: Classification and Regression Neural Network for Facial Beauty Prediction.
CRNet achieves the state-of-the-art performance on SCUT-FBP and ECCV HotOrNot dataset. For more details, please read our paper.
| Methods | PC |
|---|---|
| Gaussian Regression | 0.6482 |
| CNN-based | 0.8187 |
| PI-CNN | 0.87 |
| Liu et al. | 0.6938 |
| CRNet | 0.8723 |
| Methods | PC |
|---|---|
| Multi-scale Model | 0.458 |
| S. Wang et al. | 0.437 |
| CRNet | 0.482 |
The most attractive parts learned by CRNet is shown as follows.
A updated version of CRNet with huge improvement can be found from ComboLoss.
If you find the code or the experimental results useful in your research, please cite our paper:
@inproceedings{xu2018crnet,
title={CRNet: Classification and Regression Neural Network for Facial Beauty Prediction},
author={Xu, Lu and Xiang, Jinhai and Yuan, Xiaohui},
booktitle={Pacific Rim Conference on Multimedia},
pages={661--671},
year={2018},
organization={Springer}
}