Learning distance metric regression for facial age estimation

This paper proposes a novel regression method based on distance metric learning for human age estimation. We take age estimation as a problem of distance-based ordinal regression, in which the facial aging trend can be discovered by a learned distance metric. Through the learned distance metric, we hope that both the ordinal information of different age groups and the local geometry structure of the target neighborhoods can be well preserved simultaneously. Then, the facial aging trend can be truly discovered by the learned metric. Experimental results on the publicly available FG-NET database are very competitive against the state-of-the-art methods.