Age estimation is a challenging task not only because face appearance is affected by illumination, pose, and expression, but also because there exists age label ambiguity among different demographic groups. In this work, we first revisit different label distribution learning (LDL) based age estimation methods and propose a more general formulation, which can unify individual LDL-based age estimation methods, as well as the traditional regression, classification, and ranking based age estimation methods. Based on such a general formulation, we propose a novel deep conditional distribution learning (DCDL) method, which can flexibly leverage a varying number of auxiliary face attributes to achieve adaptive age-related feature learning and improve age estimation robustness against the challenges above. Experimental results on multiple age estimation datasets (MORPH II, AgeDB, FG-NET, MegaAge-Asian, CLAP2016, UTK-Face, and LFW+) show that the proposed approach outperforms the state-of-the-art age estimation methods by a large margin. In addition, the proposed approach can generalize well to other human attributes estimation tasks, like height, weight, and body mass index (BMI) estimation.

中文翻译：

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用于年龄估计的深度条件分布学习


年龄估计是一项具有挑战性的任务，不仅因为面部外观受到光照、姿势和表情的影响，而且因为不同人口群体之间存在年龄标签模糊性。在这项工作中，我们首先重新审视基于不同标签分布学习（LDL）的年龄估计方法，并提出一个更通用的公式，它可以统一基于个体 LDL 的年龄估计方法以及传统的基于回归、分类和排名的年龄估计方法。基于这样的通用公式，我们提出了一种新颖的深度条件分布学习（DCDL）方法，该方法可以灵活地利用不同数量的辅助面部属性来实现自适应年龄相关特征学习，并提高年龄估计针对上述挑战的鲁棒性。在多个年龄估计数据集（MORPH II、AgeDB、FG-NET、MegaAge-Asian、CLAP2016、UTK-Face 和 LFW+）上的实验结果表明，所提出的方法大大优于最先进的年龄估计方法。利润。此外，所提出的方法可以很好地推广到其他人类属性估计任务，例如身高、体重和体重指数（BMI）估计。