# Quantifying Facial Age by Posterior of Age Comparisons

arXiv: https://arxiv.org/abs/1708.09687 · HTML: https://mlsanity.com/paper/1708.09687/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Aug 2017.

## Built on by (most cited first, 16 of 16)

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- [Masked Contrastive Graph Representation Learning for Age Estimation](https://mlsanity.com/paper/2306.17798/) (2023)
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- [A Call to Reflect on Evaluation Practices for Age Estimation: Comparative Analysis of the State-of-the-Art and a Unified Benchmark](https://mlsanity.com/paper/2307.04570/) (2023)
- [CILF-CIAE: CLIP-driven Image-Language Fusion for Correcting Inverse Age Estimation](https://mlsanity.com/paper/2312.01758/) (2023)
- [You Only Learn One Query: Learning Unified Human Query for Single-Stage Multi-Person Multi-Task Human-Centric Perception](https://mlsanity.com/paper/2312.05525/) (2023)
- [Beyond Specialization: Assessing the Capabilities of MLLMs in Age and Gender Estimation](https://mlsanity.com/paper/2403.02302/) (2024)
- [DAA: A Delta Age AdaIN operation for age estimation via binary code transformer](https://mlsanity.com/paper/2303.07929/) (2023)
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- [Does Face Recognition Error Echo Gender Classification Error?](https://mlsanity.com/paper/2104.13803/) (2021)
- [Lipschitzness Effect of a Loss Function on Generalization Performance of Deep Neural Networks Trained by Adam and AdamW Optimizers](https://mlsanity.com/paper/2303.16464/) (2023)
- [Towards Inclusive Face Recognition Through Synthetic Ethnicity Alteration](https://mlsanity.com/paper/2405.01273/) (2024)
- [P-Age: Pexels Dataset for Robust Spatio-Temporal Apparent Age Classification](https://mlsanity.com/paper/2311.02432/) (2023)
- [using multiple losses for accurate facial age estimation](https://mlsanity.com/paper/2106.09393/) (2021)
- [Age Prediction From Face Images Via Contrastive Learning](https://mlsanity.com/paper/2308.11896/) (2023)
- [Foundation Artificial Intelligence Models for Health Recognition Using Face Photographs (FAHR-Face)](https://mlsanity.com/paper/2506.14909/) (2025)

## Abstract (arXiv)

We introduce a novel approach for annotating large quantity of in-the-wild facial images with high-quality posterior age distribution as labels. Each posterior provides a probability distribution of estimated ages for a face. Our approach is motivated by observations that it is easier to distinguish who is the older of two people than to determine the person's actual age. Given a reference database with samples of known ages and a dataset to label, we can transfer reliable annotations from the former to the latter via human-in-the-loop comparisons. We show an effective way to transform such comparisons to posterior via fully-connected and SoftMax layers, so as to permit end-to-end training in a deep network. Thanks to the efficient and effective annotation approach, we collect a new large-scale facial age dataset, dubbed `MegaAge', which consists of 41,941 images. Data can be downloaded from our project page mmlab.ie.cuhk.edu.hk/projects/MegaAge and github.com/zyx2012/Age_estimation_BMVC2017. With the dataset, we train a network that jointly performs ordinal hyperplane classification and posterior distribution learning. Our approach achieves state-of-the-art results on popular benchmarks such as MORPH2, Adience, and the newly proposed MegaAge.