# SCUT-FBP: A Benchmark Dataset for Facial Beauty Perception

arXiv: https://arxiv.org/abs/1511.02459 · HTML: https://mlsanity.com/paper/1511.02459/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Nov 2015.

## Built on by (most cited first, 15 of 15)

- [Inaccurate Label Distribution Learning](https://mlsanity.com/paper/2302.13000/) (2023)
- [Generative Pre-training for Subjective Tasks: A Diffusion Transformer-Based Framework for Facial Beauty Prediction](https://mlsanity.com/paper/2507.20363/) (2025)
- [Mamba-CNN: A Hybrid Architecture for Efficient and Accurate Facial Beauty Prediction](https://mlsanity.com/paper/2509.01431/) (2025)
- [Label Distribution Learning from Logical Label](https://mlsanity.com/paper/2303.06847/) (2023)
- [Ethically aligned Deep Learning: Unbiased Facial Aesthetic Prediction](https://mlsanity.com/paper/2111.05149/) (2021)
- [Lightweight Facial Attractiveness Prediction Using Dual Label Distribution](https://mlsanity.com/paper/2212.01742/) (2022)
- [No Regularization is Needed: An Efficient and Effective Model for Incomplete Label Distribution Learning](https://mlsanity.com/paper/2308.07047/) (2023)
- [MetaFBP: Learning to Learn High-Order Predictor for Personalized Facial Beauty Prediction](https://mlsanity.com/paper/2311.13929/) (2023)
- ["My Kind of Woman": Analysing Gender Stereotypes in AI through The Averageness Theory and EU Law](https://mlsanity.com/paper/2407.17474/) (2024)
- [Improving Multi-Label Contrastive Learning by Leveraging Label Distribution](https://mlsanity.com/paper/2501.19145/) (2025)
- [Uncertainty-oriented Order Learning for Facial Beauty Prediction](https://mlsanity.com/paper/2409.00603/) (2024)
- [Towards Better Performance in Incomplete LDL: Addressing Data Imbalance](https://mlsanity.com/paper/2410.13579/) (2024)
- [From Distance to Direction: Structure-aware Label-specific Feature Fusion for Label Distribution Learning](https://mlsanity.com/paper/2504.19374/) (2025)
- [Multi-View Semi-Supervised Label Distribution Learning with Local Structure Complementarity](https://mlsanity.com/paper/2510.13917/) (2025)
- [Facial beauty prediction fusing transfer learning and broad learning system](https://mlsanity.com/paper/2603.16930/) (2026)

## Abstract (arXiv)

In this paper, a novel face dataset with attractiveness ratings, namely, the SCUT-FBP dataset, is developed for automatic facial beauty perception. This dataset provides a benchmark to evaluate the performance of different methods for facial attractiveness prediction, including the state-of-the-art deep learning method. The SCUT-FBP dataset contains face portraits of 500 Asian female subjects with attractiveness ratings, all of which have been verified in terms of rating distribution, standard deviation, consistency, and self-consistency. Benchmark evaluations for facial attractiveness prediction were performed with different combinations of facial geometrical features and texture features using classical statistical learning methods and the deep learning method. The best Pearson correlation (0.8187) was achieved by the CNN model. Thus, the results of our experiments indicate that the SCUT-FBP dataset provides a reliable benchmark for facial beauty perception.