# using multiple losses for accurate facial age estimation

arXiv: https://arxiv.org/abs/2106.09393 · HTML: https://mlsanity.com/paper/2106.09393/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 21 references that are in the map.
- First posted on arXiv 17 Jun 2021.

## Builds on (most cited first, 8 of 8)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks](https://mlsanity.com/paper/1604.02878/) (2016)
- [Label Distribution Learning](https://mlsanity.com/paper/1408.6027/) (2014)
- [Convolutional Networks with Dense Connectivity](https://mlsanity.com/paper/2001.02394/) (2020)
- [Quantifying Facial Age by Posterior of Age Comparisons](https://mlsanity.com/paper/1708.09687/) (2017)
- [Elastic Neural Networks for Classification](https://mlsanity.com/paper/1810.00589/) (2018)
- [Elastic Neural Networks: A Scalable Framework for Embedded Computer Vision](https://mlsanity.com/paper/1807.00453/) (2018)

## Abstract (arXiv)

Age estimation is an essential challenge in computer vision. With the advances of convolutional neural networks, the performance of age estimation has been dramatically improved. Existing approaches usually treat age estimation as a classification problem. However, the age labels are ambiguous, thus make the classification task difficult. In this paper, we propose a simple yet effective approach for age estimation, which improves the performance compared to classification-based methods. The method combines four classification losses and one regression loss representing different class granularities together, and we name it as Age-Granularity-Net. We validate the Age-Granularity-Net framework on the CVPR Chalearn 2016 dataset, and extensive experiments show that the proposed approach can reduce the prediction error compared to any individual loss. The source code link is https://github.com/yipersevere/age-estimation.