# Uncertainty-oriented Order Learning for Facial Beauty Prediction

arXiv: https://arxiv.org/abs/2409.00603 · HTML: https://mlsanity.com/paper/2409.00603/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 63 references that are in the map.
- First posted on arXiv 1 Sep 2024.

## Builds on (most cited first, 14 of 14)

- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Optimal Transport for Domain Adaptation](https://mlsanity.com/paper/1507.00504/) (2015)
- [Return of Frustratingly Easy Domain Adaptation](https://mlsanity.com/paper/1511.05547/) (2015)
- [Variational Autoencoders for Collaborative Filtering](https://mlsanity.com/paper/1802.05814/) (2018)
- [Probabilistic Face Embeddings](https://mlsanity.com/paper/1904.09658/) (2019)
- [Data Uncertainty Learning in Face Recognition](https://mlsanity.com/paper/2003.11339/) (2020)
- [Implicit Semantic Data Augmentation for Deep Networks](https://mlsanity.com/paper/1909.12220/) (2019)
- [Lightweight Probabilistic Deep Networks](https://mlsanity.com/paper/1805.11327/) (2018)
- [On Feature Normalization and Data Augmentation](https://mlsanity.com/paper/2002.11102/) (2020)
- [Learning Probabilistic Ordinal Embeddings for Uncertainty-Aware Regression](https://mlsanity.com/paper/2103.13629/) (2021)
- [SCUT-FBP5500: A Diverse Benchmark Dataset for Multi-Paradigm Facial Beauty Prediction](https://mlsanity.com/paper/1801.06345/) (2018)
- [SCUT-FBP: A Benchmark Dataset for Facial Beauty Perception](https://mlsanity.com/paper/1511.02459/) (2015)
- [Label distribution based facial attractiveness computation by deep residual learning](https://mlsanity.com/paper/1609.00496/) (2016)
- [ComboLoss for Facial Attractiveness Analysis with Squeeze-and-Excitation Networks](https://mlsanity.com/paper/2010.10721/) (2020)

## Abstract (arXiv)

Previous Facial Beauty Prediction (FBP) methods generally model FB feature of an image as a point on the latent space, and learn a mapping from the point to a precise score. Although existing regression methods perform well on a single dataset, they are inclined to be sensitive to test data and have weak generalization ability. We think they underestimate two inconsistencies existing in the FBP problem: 1. inconsistency of FB standards among multiple datasets, and 2. inconsistency of human cognition on FB of an image. To address these issues, we propose a new Uncertainty-oriented Order Learning (UOL), where the order learning addresses the inconsistency of FB standards by learning the FB order relations among face images rather than a mapping, and the uncertainty modeling represents the inconsistency in human cognition. The key contribution of UOL is a designed distribution comparison module, which enables conventional order learning to learn the order of uncertain data. Extensive experiments on five datasets show that UOL outperforms the state-of-the-art methods on both accuracy and generalization ability.