  We propose an approach for unsupervised domain adaptation for the task of
estimating someone's age from a given face image. In order to avoid the
propagation of racial bias in most publicly available face image datasets into
the inefficacy of models trained on them, we perform domain adaptation to
motivate the predictor to learn features that are invariant to ethnicity,
enhancing the generalization performance across faces of people from different
ethnic backgrounds. Exploiting the ordinality of age, we also impose ranking
constraints on the prediction of the model and design our model such that it
takes as input a pair of images, and outputs both the relative age difference
and the rank of the first identity with respect to the other in terms of their
ages. Furthermore, we implement Multi-Dimensional Scaling to retrieve absolute
ages from the predicted age differences from as few as two labeled images from
the domain to be adapted to. We experiment with a publicly available dataset
with age labels, dividing it into subsets based on the ethnicity labels, and
evaluating the performance of our approach on the data from an ethnicity
different from the one that the model is trained on. Additionally, we impose a
constraint to preserve the sanity of the predictions with respect to relative
and absolute ages, and another to ensure the smoothness of the predictions with
respect to the input. We experiment extensively and compare various domain
adaptation approaches for the task of regression.
