Owing to the variations including both intrinsic and extrinsic factors, age estimation remains a challenging problem. In this paper, five cascaded structure frameworks are proposed for age estimation based on convolutional neural networks. All frameworks are learned and guided by auxiliary demographic information, since other demographic information (i.e., gender and race) is beneficial for age prediction. Each cascaded structure framework is embodied in a parent network and several subnetworks. For example, one of the applied framework is a gender classifier trained by gender information, and then two subnetworks are trained by the male and female samples, respectively. Furthermore, we use the features extracted from the cascaded structure frameworks with Gaussian process regression that can boost the performance further for age estimation. Experimental results on the MORPH II and CACD datasets have gained superior performances compared to the state-of-the-art methods. The mean absolute error is significantly reduced from 3.63 to 2.93 years under the same test protocol on the MORPH II dataset.

中文翻译：

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辅助人口统计信息辅助级联结构年龄估计


由于内在和外在因素的变化，年龄估计仍然是一个具有挑战性的问题。本文提出了五种基于卷积神经网络的年龄估计级联结构框架。所有框架都是由辅助人口统计信息学习和指导的，因为其他人口统计信息（即性别和种族）有利于年龄预测。每个级联结构框架都体现在一个父网络和多个子网络中。例如，应用的框架之一是通过性别信息训练性别分类器，然后分别通过男性和女性样本训练两个子网络。此外，我们使用从级联结构框架中提取的特征和高斯过程回归，可以进一步提高年龄估计的性能。与最先进的方法相比，MORPH II 和 CACD 数据集上的实验结果获得了优越的性能。在 MORPH II 数据集上的相同测试协议下，平均绝对误差从 3.63 年显着减少到 2.93 年。