This repo provides the source code for our paper Transferring Rich Deep Features for Facial Beauty Prediction. This code has been tested on Ubuntu16 .04 with TensorFlow0.12.0, a newer version may bring you some trouble since TensorFlow's APIs always change after releasing a new version.
Our proposed two-stage method achieves state-of-the-art performance on SCUT-FBP and Female Facial Beauty Dataset (ECCV2010) v1.0 dataset. TransFBP also achieves very competitive performance on SCUT-FBP5500 dataset.
- Evaluation with the SCUT-FBP Dataset
| Methods | PC |
|---|---|
| Combined Features+Gaussian Reg | 0.6482 |
| CNN-based | 0.8187 |
| Liu et al. | 0.6938 |
| KFME | 0.7988 |
| RegionScatNet | 0.83 |
| PI-CNN | 0.87 |
| TransFBP (Ours) | 0.8742 |
- Evaluation with the HotOrNot Dataset
| Methods | PC |
|---|---|
| Eigenface | 0.180 |
| Multiscale Model | 0.458 |
| Auto Encoder | 0.437 |
| TransFBP (Ours) | 0.468 |
- Evaluation with the SCUT-FBP5500 Dataset
| Methods | PC |
|---|---|
| Geometric features + Gaussian Regression | 0.6738 |
| Geometric features + SVR | 0.6668 |
| 64UniSample + SVR | 0.8065 |
| AlexNet | 0.8298 |
| ResNet18 | 0.8513 |
| ResNeXt50 | 0.8777 |
| TransFBP (Ours) | 0.8519 |
If you find the code or the experimental results useful in your research, please consider citing our paper as:
@article{xu2018transferring,
title={Transferring Rich Deep Features for Facial Beauty Prediction},
author={Xu, Lu and Xiang, Jinhai and Yuan, Xiaohui},
journal={arXiv preprint arXiv:1803.07253},
year={2018}
}