  In practice, and especially when training deep neural networks, visual
recognition rules are often learned based on various sources of information. On
the other hand, the recent deployment of facial recognition systems with uneven
performances on different population segments has highlighted the
representativeness issues induced by a naive aggregation of the datasets. In
this paper, we show how biasing models can remedy these problems. Based on the
(approximate) knowledge of the biasing mechanisms at work, our approach
consists in reweighting the observations, so as to form a nearly debiased
estimator of the target distribution. One key condition is that the supports of
the biased distributions must partly overlap, and cover the support of the
target distribution. In order to meet this requirement in practice, we propose
to use a low dimensional image representation, shared across the image
databases. Finally, we provide numerical experiments highlighting the relevance
of our approach.
