  With the propensity for deep learning models to learn unintended signals from
data sets there is always the possibility that the network can `cheat' in order
to solve a task. In the instance of data sets for visual kinship verification,
one such unintended signal could be that the faces are cropped from the same
photograph, since faces from the same photograph are more likely to be from the
same family. In this paper we investigate the influence of this artefactual
data inference in published data sets for kinship verification.
  To this end, we obtain a large dataset, and train a CNN classifier to
determine if two faces are from the same photograph or not. Using this
classifier alone as a naive classifier of kinship, we demonstrate near state of
the art results on five public benchmark data sets for kinship verification -
achieving over 90% accuracy on one of them. Thus, we conclude that faces
derived from the same photograph are a strong inadvertent signal in all the
data sets we examined, and it is likely that the fraction of kinship explained
by existing kinship models is small.
