  Machine-learning-based age estimation has received lots of attention.
Traditional age estimation mechanism focuses estimation age error, but ignores
that there is a deviation between the estimated age and real age due to
disease. Pathological age estimation mechanism the author proposed before
introduces age deviation to solve the above problem and improves classification
capability of the estimated age significantly. However,it does not consider the
age estimation error of the normal control (NC) group and results in a larger
error between the estimated age and real age of NC group. Therefore, an
integrated age estimation mechanism based on Decision-Level fusion of error and
deviation orientation model is proposed to solve the problem.Firstly, the
traditional age estimation and pathological age estimation mechanisms are
weighted together.Secondly, their optimal weights are obtained by minimizing
mean absolute error (MAE) between the estimated age and real age of normal
people. In the experimental section, several representative age-related
datasets are used for verification of the proposed method. The results show
that the proposed age estimation mechanism achieves a good tradeoff effect of
age estimation. It not only improves the classification ability of the
estimated age, but also reduces the age estimation error of the NC group. In
general, the proposed age estimation mechanism is effective. Additionally, the
mechanism is a framework mechanism that can be used to construct different
specific age estimation algorithms, contributing to relevant research.
