Cross-heterogeneous-database age estimation through correlation representation learning
Cross-heterogeneous-database age estimation through correlation representation learning
复制标题
通过相关表示学习进行跨异构数据库年龄估计
DOI：
10.1016/j.neucom.2017.01.064
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发表时间：
2017-05-17
期刊：
影响因子：
6
通讯作者：
Chen, Songcan
中科院分区：
文献类型：
--
作者：
Tian, Qing;Chen, Songcan
人体年龄估计是一个重要的研究课题，在商品推荐、安全监控等场景中都有应用。现有估计器的设计通常遵循相同的流程，即从给定的训练数据集构建估计器，然后在同一数据集的holdout测试集上进行评估，以显示其有效性。在这样做的过程中，隐含的假设是训练集和测试集应该具有相同的年龄分布和特征表示，因此-意味着(1)一旦待测试的人脸图像的年龄超出训练集的年龄范围，错误估计自然是不可避免的；(2)基于特定数据集构建的估计器通常不能直接应用于其他数据集的评估，因为它们的特征表示的维度和类型通常是不同的（即这些数据集是异构的）。也就是说，现有的方法不能直接用于跨异构数据集的年龄估计。据我们所知，现有老化数据集的年龄分布通常不是一致的，而是相互补充的。基于不同数据集在年龄分布上的这种互补性，我们开发了一种所谓的相关分量流形空间学习（CCMSL），首先通过捕获异构数据库之间的相关性来学习一个共同的特征空间，然后在得到的空间中通过相关表示学习（CRL）建立一个跨异构数据集的单一年龄估计器。这样不仅可以补偿单个老化数据集的年龄分布不完备性，而且可以增强估计器的判别能力。最后，实验结果验证了所提方法的优越性。(C) 2017 Elsevier B.V.版权所有
Human age estimation is an important research topic and has found its applications in such scenarios as commodity recommendation and security monitoring. The design of existing estimators generally follows a same pipeline, i.e., an estimator is built from a given training dataset and then evaluated on a holdout testing set from the same dataset to display its effectiveness. In doing so, an implicit assumption is that both training and testing sets should share the same age distribution and feature representation, consequently -meaning that (1) once the age of a face image to be tested is outside the age range of training set, a mis-estimation is naturally inevitable; (2) an estimator built on a specific dataset usually cannot be directly applied to make evaluations on other datasets, because the dimensions and types of their feature representations are usually different (i.e., these datasets are heterogeneous). That is, existing methods can not be directly employed to perform cross-heterogeneous-dataset age estimation. To the best of our knowledge, the age distributions of existing aging datasets are usually not consistent but complementary to each other. Motivated by such a complementarity characteristic of different datasets in age distributions, we develop a so-called correlation component manifold space learning (CCMSL) to first learn a common feature space by capturing the correlations between the heterogeneous databases, and then in the resulting space establish a single age estimator across such heterogeneous datasets through correlation representation learning (CRL). As a result, not only can the age -distribution -incompleteness of individual aging datasets be compensated, but also the discriminating ability of the estimator be reinforced. Finally, experimental results demonstrate the superiority of the proposed methods. (C) 2017 Elsevier B.V. All rights reserved.