Exploiting Unlabeled Ages for Aging Pattern Analysis on a Large Database
Exploiting Unlabeled Ages for Aging Pattern Analysis on a Large Database
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DOI：
10.1109/cvprw.2013.75
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发表时间：
2013-06
期刊：
影响因子：
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通讯作者：
Chao Zhang-;G. Guo
中科院分区：
文献类型：
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作者：
Chao Zhang-;G. Guo
大数据分析是计算机视觉和模式识别领域的一个新兴课题。作为大数据的一个例子，我们研究了语义年龄标签和面部老化模式分析在一个大型数据库。在年龄分析中，最大的挑战之一是缺乏大量具有真实年龄标签的人脸图像。与许多其他基于示例的识别问题不同，在这些问题中，人类注释可以用作训练和测试的地面真值标签，人类注释者很难在人脸图像中标记确切的年龄。另一种方法是利用未标记的年龄来提高年龄估计性能。然而，目前还不清楚是否可以使用未标记的人脸图像的年龄估计，以及如何使用未标记的数据。本文从两个方面对这两个问题进行了全面的研究：半监督学习和无监督学习的老化模式分析。我们强调使用真实年龄标签和大型数据库的重要性，以便在大数据的背景下得出有意义的措施。我们的研究可以对收集老化模式产生影响，这在实践中非常昂贵和耗时。
Big Data analysis is an emerging topic in computer vision and pattern recognition. As one example problem of big data, we study semantic age labels and facial aging pattern analysis on a large database. In aging analysis, one of the great challenges is the lack of a large number of face images with ground truth age labels. Unlike many other example-based recognition problems where human annotations can be used as the ground truth labels for both training and testing, it is quite difficult to label the exact ages in face images by human annotators. An alternative is to exploit the unlabeled ages to enhance the age estimation performance. However, it is unclear whether the face images with unlabeled ages can be used or not for age estimation, and how to use the unlabeled data. In this paper, we study the two problems comprehensively under two paradigms: the semi-supervised learning and unsupervised learning for aging pattern analysis. We emphasize the importance of using ground truth age labels and a large database in order to derive a meaningful measure in the context of big data. Our study can make an impact on collecting aging patterns that is very expensive and time consuming in practice.