  Human age estimation has attracted increasing researches due to its wide
applicability in such as security monitoring and advertisement recommendation.
Although a variety of methods have been proposed, most of them focus only on
the age-specific facial appearance. However, biological researches have shown
that not only gender but also the aging difference between the male and the
female inevitably affect the age estimation. To our knowledge, so far there
have been two methods that have concerned the gender factor. The first is a
sequential method which first classifies the gender and then performs age
estimation respectively for classified male and female. Although it promotes
age estimation performance because of its consideration on the gender semantic
difference, an accumulation risk of estimation errors is unavoidable. To
overcome drawbacks of the sequential strategy, the second is to regress the age
appended with the gender by concatenating their labels as two dimensional
output using Partial Least Squares (PLS). Although leading to promotion of age
estimation performance, such a concatenation not only likely confuses the
semantics between the gender and age, but also ignores the aging discrepancy
between the male and the female. In order to overcome their shortcomings, in
this paper we propose a unified framework to perform gender-aware age
estimation. The proposed method considers and utilizes not only the semantic
relationship between the gender and the age, but also the aging discrepancy
between the male and the female. Finally, experimental results demonstrate not
only the superiority of our method in performance, but also its good
interpretability in revealing the aging discrepancy.
