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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. 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

Experiments

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

Examples

eccv_pred

Resources

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}
}

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Official Code for Paper <Transferring Rich Deep Features for Facial Beauty Prediction> (arXiv1803.07253)

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