Locally Adjusted Robust Regression for Human Age Estimation

Authors:
Guodong Guo;Yun Fu;Thomas S. Huang;Charles R. Dyer
Affiliations:
Computer Science, NCCU, Durham, NC, 27707, gdguo@nccu.edu;Beckman Institute, UIUC, Urbana, IL 61801, yunfu2@uiuc.edu;Beckman Institute, UIUC, Urbana, IL 61801, t-huang1@uiuc.edu;Computer Sciences, UW-Madison, Madison, WI, 53706, dyer@cs.wisc.edu
Venue:
WACV '08 Proceedings of the 2008 IEEE Workshop on Applications of Computer Vision
Year:
2008

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Abstract

Automatic human age estimation has considerable potential applications in human computer interaction and multimedia communication. However, the age estimation problem is challenging. We design a locally adjusted robust regressor (LARR) for learning and prediction of human ages. The novel approach reduces the age estimation errors significantly over all previous methods. Experiments on two aging databases show the success of the proposed method for human aging estimation.