TY - GEN
T1 - A study on automatic age estimation using a large database
AU - Guo, Guodong
AU - Mu, Guowang
AU - Fu, Yun
AU - Dyer, Charles
AU - Huang, Thomas
PY - 2009
Y1 - 2009
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=77953227243&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77953227243&partnerID=8YFLogxK
U2 - 10.1109/ICCV.2009.5459438
DO - 10.1109/ICCV.2009.5459438
M3 - Conference contribution
AN - SCOPUS:77953227243
SN - 9781424444205
T3 - Proceedings of the IEEE International Conference on Computer Vision
SP - 1986
EP - 1991
BT - 2009 IEEE 12th International Conference on Computer Vision, ICCV 2009
T2 - 12th International Conference on Computer Vision, ICCV 2009
Y2 - 29 September 2009 through 2 October 2009
ER -