Papers › Transferring Rich Deep Features for Facial Beauty Prediction
Transferring Rich Deep Features for Facial Beauty Prediction
Lu Xu, Jinhai Xiang, Xiaohui Yuan
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.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Facial Beauty Prediction | ECCV HotOrNot | CNN features + Bayesian ridge regression | Pearson Correlation | 0.468 | #1 of 1 | Archive leaderboard | report |
| Facial Beauty Prediction | SCUT-FBP | CNN features + Bayesian ridge regression | MAE | 0.2595 | #1 of 2 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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