# Label distribution based facial attractiveness computation by deep residual learning

arXiv: https://arxiv.org/abs/1609.00496 · HTML: https://mlsanity.com/paper/1609.00496/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Sep 2016.

## Built on by (most cited first, 12 of 12)

- [Towards Unbiased Label Distribution Learning for Facial Pose Estimation Using Anisotropic Spherical Gaussian](https://mlsanity.com/paper/2208.09122/) (2022)
- [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)
- [Frequency Disentangled Residual Network](https://mlsanity.com/paper/2109.12556/) (2021)
- [Scale-interaction transformer: a hybrid cnn-transformer model for facial beauty prediction](https://mlsanity.com/paper/2509.05078/) (2025)
- [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)
- [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)
- [Uncertainty-oriented Order Learning for Facial Beauty Prediction](https://mlsanity.com/paper/2409.00603/) (2024)
- [Facial Attractiveness Prediction in Live Streaming: A New Benchmark and Multi-modal Method](https://mlsanity.com/paper/2501.02509/) (2025)
- [VM-BeautyNet: A Synergistic Ensemble of Vision Transformer and Mamba for Facial Beauty Prediction](https://mlsanity.com/paper/2510.16220/) (2025)

## Abstract (arXiv)

Two challenges lie in the facial attractiveness computation research: the lack of true attractiveness labels (scores), and the lack of an accurate face representation. In order to address the first challenge, this paper recasts facial attractiveness computation as a label distribution learning (LDL) problem rather than a traditional single-label supervised learning task. In this way, the negative influence of the label incomplete problem can be reduced. Inspired by the recent promising work in face recognition using deep neural networks to learn effective features, the second challenge is expected to be solved from a deep learning point of view. A very deep residual network is utilized to enable automatic learning of hierarchical aesthetics representation. Integrating these two ideas, an end-to-end deep learning framework is established. Our approach achieves the best results on a standard benchmark SCUT-FBP dataset compared with other state-of-the-art work.