# Soft-ranking Label Encoding for Robust Facial Age Estimation

arXiv: https://arxiv.org/abs/1906.03625 · HTML: https://mlsanity.com/paper/1906.03625/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jun 2019.

## Built on by (most cited first, 8 of 8)

- [Teacher-Student Training and Triplet Loss to Reduce the Effect of Drastic Face Occlusion](https://mlsanity.com/paper/2111.10561/) (2021)
- [Hierarchical Attention-based Age Estimation and Bias Estimation](https://mlsanity.com/paper/2103.09882/) (2021)
- [Adaptive Mean-Residue Loss for Robust Facial Age Estimation](https://mlsanity.com/paper/2203.17156/) (2022)
- [LAE : Long-tailed Age Estimation](https://mlsanity.com/paper/2110.12741/) (2021)
- [General vs. Long-Tailed Age Estimation: An Approach to Kill Two Birds with One Stone](https://mlsanity.com/paper/2307.10129/) (2023)
- [A Survey of Face Recognition](https://mlsanity.com/paper/2212.13038/) (2022)
- [DiffClean: Diffusion-based Makeup Removal for Accurate Age Estimation](https://mlsanity.com/paper/2507.13292/) (2025)
- [Integrating ConvNeXt and Vision Transformers for Enhancing Facial Age Estimation](https://mlsanity.com/paper/2511.00123/) (2025)

## Abstract (arXiv)

Automatic facial age estimation can be used in a wide range of real-world applications. However, this process is challenging due to the randomness and slowness of the aging process. Accordingly, in this paper, we propose a comprehensive framework aimed at overcoming the challenges associated with facial age estimation. First, we propose a novel age encoding method, referred to as 'Soft-ranking', which encodes two important properties of facial age, i.e., the ordinal property and the correlation between adjacent ages. Therefore, Soft-ranking provides a richer supervision signal for training deep models. Moreover, we also carefully analyze existing evaluation protocols for age estimation, finding that the overlap in identity between the training and testing sets affects the relative performance of different age encoding methods. Finally, since existing face databases for age estimation are generally small, deep models tend to suffer from an overfitting problem. To address this issue, we propose a novel regularization strategy to encourage deep models to learn more robust features from facial parts for age estimation purposes. Extensive experiments indicate that the proposed techniques improve the age estimation performance; moreover, we achieve state-of-the-art performance on the three most popular age databases, i.e., Morph II, CLAP2015, and CLAP2016.