  In this paper, we propose a novel age estimation method based on GLOH feature
descriptor and multi-task learning (MTL). The GLOH feature descriptor, one of
the state-of-the-art feature descriptor, is used to capture the age-related
local and spatial information of face image. As the exacted GLOH features are
often redundant, MTL is designed to select the most informative feature bins
for age estimation problem, while the corresponding weights are determined by
ridge regression. This approach largely reduces the dimensions of feature,
which can not only improve performance but also decrease the computational
burden. Experiments on the public available FG-NET database show that the
proposed method can achieve comparable performance over previous approaches
while using much fewer features.
