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ICCV
2009
IEEE

A study on automatic age estimation using a large database

8 years 2 months ago
A study on automatic age estimation using a large database
In this paper we study some problems related to human age estimation using a large database. First, we study the influence of gender on age estimation based on face representations that combine biologically-inspired features with manifold learning techniques. Second, we study age estimation using smaller gender and age groups rather than on all ages. Significant error reductions are observed in both cases. Based on these results, we designed three frameworks for automatic age estimation that exhibit high performance. Unlike previous methods that require manual separation of males and females prior to age estimation, our work is the first to estimate age automatically on a large database. Furthermore, a data fusion approach is proposed using one of the frameworks, which gives an age estimation error more than 40% smaller than previous methods.
Guodong Guo, Guowang Mu, Yun Fu, Charles R. Dyer,
Added 18 Feb 2011
Updated 18 Feb 2011
Type Journal
Year 2009
Where ICCV
Authors Guodong Guo, Guowang Mu, Yun Fu, Charles R. Dyer, Thomas S. Huang
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