通过参考相似年龄人脸，迭代优化单张人脸的年龄估计。
Relative Age Estimation Using Face Images
- 用差分回归建模年龄差异，基于参考库改进初始估计
- 在MORPH II和CACD数据集上达到最新最好性能
- 可发现并缓解现有方法的年龄估计偏差
本文提出一种新的深度学习方法，通过引入已知年龄的参考人脸数据库，对单张人脸图像的初始年龄估计进行精细化修正。该方法利用网络预测输入图像与参考图像之间的年龄差，从而提升估计精度。通过差分回归显式建模随年龄变化的面部特征，相较传统绝对年龄估计有明显改进。此外，我们设计了一种年龄增强方案，在训练中迭代建模初始估计的误差分布，进一步优化结果。实验表明，该方法在MORPH II和CACD数据集上均取得当前最优表现。同时，研究揭示了现有先进方法中存在的系统性偏差问题。
原文摘要 · Abstract (English)
This work introduces a novel deep-learning approach for estimating age from a single facial image by refining an initial age estimate. The refinement leverages a reference face database of individuals with similar ages and appearances. We employ a network that estimates age differences between an input image and reference images with known ages, thus refining the initial estimate. Our method explicitly models age-dependent facial variations using differential regression, yielding improved accuracy compared to conventional absolute age estimation. Additionally, we introduce an age augmentation scheme that iteratively refines initial age estimates by modeling their error distribution during training. This iterative approach further enhances the initial estimates. Our approach surpasses existing methods, achieving state-of-the-art accuracy on the MORPH II and CACD datasets. Furthermore, we examine the biases inherent in contemporary state-of-the-art age estimation techniques.
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