Label-Sensitive Deep Metric Learning for Facial Age Estimation
In this paper, we present a label-sensitive deep metric learning (LSDML) approach for facial age estimation. Motivated by the fact that human age labels are chronologically correlated, our proposed LSDML aims to seek a series of hierarchical nonlinear transformations by deep residual network to project face samples to a latent common space, where the similarity of face pairs is equivalently isotonic to the age difference in a ranking-preserving manner. Since traversal access to total negative samples catastrophically costs and leads to suboptimal, our model learns to mine hard meaningful samples in parallel to learning feature similarity, so that the local manifold of face samples is preserved in the transformed subspace. To better improve the performance on the data set that contains few labeled samples, we further extend our LSDML to a multi-source LSDML method, which aims at maximizing the cross-population correlation of different face aging data sets. Extensive experimental results on four benchmarking data sets show the effectiveness of our proposed approach.