Facial age estimation based on label-sensitive learning and age-oriented regression

This paper provides a new age estimation approach, which distinguishes itself with the following three contributions. First, we combine distance metric learning and dimensionality reduction to better explore the connections between facial features and age labels. Second, to exploit the intrinsic ordinal relationship among human ages and overcome the potential data imbalance problem, a label-sensitive concept and several imbalance treatments are introduced in the system training phase. Finally, an age-oriented local regression is presented to capture the complicated facial aging process for age determination. The simulation results show that our approach achieves the lowest estimation error against existing methods.

► A new age estimation approach consisting of four steps is presented in this paper.
► A local regression algorithm is proposed to capture the complicated aging process.
► Performing supervised distance metric adjustment improves the overall results.
► The correlations among ages and the imbalance problem should be considered.
► Our approach achieves the lowest estimation error against existing methods.