Auxiliary Demographic Information Assisted Age Estimation With Cascaded Structure
Auxiliary Demographic Information Assisted Age Estimation With Cascaded Structure
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DOI：
10.1109/tcyb.2017.2741998
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发表时间：
2018-01
影响因子：
11.8
通讯作者：
Jun Wan;Zichang Tan;Zhen Lei;G. Guo;S. Li
中科院分区：
文献类型：
--
作者：
Jun Wan;Zichang Tan;Zhen Lei;G. Guo;S. Li
由于包括内在因素和外在因素在内的各种因素，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的级联结构框架。所有框架都由辅助人口信息学习和指导，因为其他人口信息(即性别和种族)对年龄预测是有益的。每个级联结构框架体现在一个父网络和几个子网络中。例如，其中一个应用框架是由性别信息训练的性别分类器，然后分别由男性样本和女性样本训练两个子网络。此外，我们将从级联结构框架中提取的特征与高斯过程回归相结合，可以进一步提高年龄估计的性能。在Morph II和CACD数据集上的实验结果比最先进的方法获得了更好的性能。在相同的测试方案下，在Morph II数据集上，平均绝对误差从3.63年显著降低到2.93年。
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.