  In this paper, we propose a progressive margin loss (PML) approach for
unconstrained facial age classification. Conventional methods make strong
assumption on that each class owns adequate instances to outline its data
distribution, likely leading to bias prediction where the training samples are
sparse across age classes. Instead, our PML aims to adaptively refine the age
label pattern by enforcing a couple of margins, which fully takes in the
in-between discrepancy of the intra-class variance, inter-class variance and
class center. Our PML typically incorporates with the ordinal margin and the
variational margin, simultaneously plugging in the globally-tuned deep neural
network paradigm. More specifically, the ordinal margin learns to exploit the
correlated relationship of the real-world age labels. Accordingly, the
variational margin is leveraged to minimize the influence of head classes that
misleads the prediction of tailed samples. Moreover, our optimization carefully
seeks a series of indicator curricula to achieve robust and efficient model
training. Extensive experimental results on three face aging datasets
demonstrate that our PML achieves compelling performance compared to state of
the arts. Code will be made publicly.
