# TransFBP

**Repository Path**: linkchainiii/TransFBP

## Basic Information

- **Project Name**: TransFBP
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-03-28
- **Last Updated**: 2021-03-28

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Transferring Rich Deep Features for Facial Beauty Prediction

## Introduction
This repo provides the source code for our paper [Transferring Rich Deep 
Features for Facial Beauty Prediction](https://arxiv.org/pdf/1803.07253.pdf). 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.

## Proposed Method
![pipeline](./architecture.png)

## Experiments
Our proposed two-stage method achieves state-of-the-art performance on [SCUT-FBP](http://www.hcii-lab.net/data/scut-fbp/en/introduce.html) and [Female Facial Beauty Dataset (ECCV2010) v1.0](https://www.researchgate.net/publication/261595808_Female_Facial_Beauty_Dataset_ECCV2010_v10) dataset.
TransFBP also achieves very competitive performance on [SCUT-FBP5500](https://arxiv.org/pdf/1801.06345.pdf) 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 |


## Examples
![eccv_pred](./eccv_pred.png)


## Resources
* [Pretrained Model on SCUT-FBP5500](https://drive.google.com/file/d/1iPr3zLbWvKlYco988Xis4IerkeSaNk5j/view?usp=sharing)
* [Pretrained VGG-Face Weights](https://drive.google.com/file/d/1Iy2MclKGL2uIlfUttHl0tT_M8SJ8wpI8/view?usp=sharing)


## Citation 
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}
}
```