Hybrid constraint SVR for facial age estimation
Hybrid constraint SVR for facial age estimation
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DOI：
10.1016/j.sigpro.2013.07.025
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
Jianyi Liu;Yao Ma;Lixin Duan;Fangfang Wang;Yuehu Liu
中科院分区：
文献类型：
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作者：
Jianyi Liu;Yao Ma;Lixin Duan;Fangfang Wang;Yuehu Liu
本文从一个新颖的角度讨论了面部年龄估计——如何联合利用监督训练数据和人工注释来提高年龄估计精度。这是由于年龄估计中缺乏数据问题和当前网络的蓬勃发展所引发的。为此，首先定义模糊年龄标签，然后将其与传统数据标签一起合并到支持向量回归（SVR）框架中。新的学习问题最终被表述为与标准 SVR 类似的对偶形式，可以使用现有的求解器轻松求解。在实验中，我们与最先进的基于回归的方法进行了比较，结果非常有竞争力。
In this paper, facial age estimation is discussed in a novel viewpoint – how to jointly exploit the supervised training data and human annotations to improve the age estimation precision. This is motivated by the lacking of data problem in age estimation and the current web booming. To do so, fuzzy age label is firstly defined, and it is then merged into the Support Vector Regression (SVR) framework together with the traditional data labels. The new learning problem is finally formulated into a similar dual form with the standard SVR, which can be easily solved using existing solvers. In experiments, we have compared with the state of the art regression based methods, and the results are very competitive.