# SCUT-FBP5500: A Diverse Benchmark Dataset for Multi-Paradigm Facial Beauty Prediction

arXiv: https://arxiv.org/abs/1801.06345 · HTML: https://mlsanity.com/paper/1801.06345/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jan 2018.

## Built on by (most cited first, 30 of 33)

- [A Survey on Bias in Visual Datasets](https://mlsanity.com/paper/2107.07919/) (2021)
- [HumanAesExpert: Advancing a Multi-Modality Foundation Model for Human Image Aesthetic Assessment](https://mlsanity.com/paper/2503.23907/) (2025)
- [Inaccurate Label Distribution Learning](https://mlsanity.com/paper/2302.13000/) (2023)
- [FairBalance: How to Achieve Equalized Odds With Data Pre-processing](https://mlsanity.com/paper/2107.08310/) (2021)
- [Interpreting Face Inference Models using Hierarchical Network Dissection](https://mlsanity.com/paper/2108.10360/) (2021)
- [Diffusion-based Facial Aesthetics Enhancement with 3D Structure Guidance](https://mlsanity.com/paper/2503.14402/) (2025)
- [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)
- [Aesthetic Attribute Assessment of Images Numerically on Mixed Multi-attribute Datasets](https://mlsanity.com/paper/2207.01806/) (2022)
- [Label Distribution Learning from Logical Label](https://mlsanity.com/paper/2303.06847/) (2023)
- [Scale-interaction transformer: a hybrid cnn-transformer model for facial beauty prediction](https://mlsanity.com/paper/2509.05078/) (2025)
- [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)
- [SynergyNet: Fusing Generative Priors and State-Space Models for Facial Beauty Prediction](https://mlsanity.com/paper/2509.17172/) (2025)
- [Learning to Select Visual In-Context Demonstrations](https://mlsanity.com/paper/2603.26775/) (2026)
- [Attribute Controllable Beautiful Caucasian Face Generation by Aesthetics Driven Reinforcement Learning](https://mlsanity.com/paper/2208.04517/) (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)
- [FairViT-GAN: A Hybrid Vision Transformer with Adversarial Debiasing for Fair and Explainable Facial Beauty Prediction](https://mlsanity.com/paper/2509.23859/) (2025)
- [Differential Parity: Relative Fairness Between Two Sets of Decisions](https://mlsanity.com/paper/2112.11279/) (2021)
- [Subjective Face Transform using Human First Impressions](https://mlsanity.com/paper/2309.15381/) (2023)
- [Uncertainty-oriented Order Learning for Facial Beauty Prediction](https://mlsanity.com/paper/2409.00603/) (2024)
- [Regression Guided Strategy to Automated Facial Beauty Optimization through Image Synthesis](https://mlsanity.com/paper/2501.00811/) (2025)
- [Facial Attractiveness Prediction in Live Streaming: A New Benchmark and Multi-modal Method](https://mlsanity.com/paper/2501.02509/) (2025)
- [Sketch-1-to-3: One Single Sketch to 3D Detailed Face Reconstruction](https://mlsanity.com/paper/2502.17852/) (2025)
- [Analysis of Bias in Deep Learning Facial Beauty Regressors](https://mlsanity.com/paper/2509.24138/) (2025)
- [A Geometric Unification of Generative AI with Manifold-Probabilistic Projection Models](https://mlsanity.com/paper/2510.00666/) (2025)
- [Multi-View Semi-Supervised Label Distribution Learning with Local Structure Complementarity](https://mlsanity.com/paper/2510.13917/) (2025)
- [VM-BeautyNet: A Synergistic Ensemble of Vision Transformer and Mamba for Facial Beauty Prediction](https://mlsanity.com/paper/2510.16220/) (2025)
- [Towards Better IncomLDL: We Are Unaware of Hidden Labels in Advance](https://mlsanity.com/paper/2511.12494/) (2025)

## Abstract (arXiv)

Facial beauty prediction (FBP) is a significant visual recognition problem to make assessment of facial attractiveness that is consistent to human perception. To tackle this problem, various data-driven models, especially state-of-the-art deep learning techniques, were introduced, and benchmark dataset become one of the essential elements to achieve FBP. Previous works have formulated the recognition of facial beauty as a specific supervised learning problem of classification, regression or ranking, which indicates that FBP is intrinsically a computation problem with multiple paradigms. However, most of FBP benchmark datasets were built under specific computation constrains, which limits the performance and flexibility of the computational model trained on the dataset. In this paper, we argue that FBP is a multi-paradigm computation problem, and propose a new diverse benchmark dataset, called SCUT-FBP5500, to achieve multi-paradigm facial beauty prediction. The SCUT-FBP5500 dataset has totally 5500 frontal faces with diverse properties (male/female, Asian/Caucasian, ages) and diverse labels (face landmarks, beauty scores within [1,~5], beauty score distribution), which allows different computational models with different FBP paradigms, such as appearance-based/shape-based facial beauty classification/regression model for male/female of Asian/Caucasian. We evaluated the SCUT-FBP5500 dataset for FBP using different combinations of feature and predictor, and various deep learning methods. The results indicates the improvement of FBP and the potential applications based on the SCUT-FBP5500.