  We propose a novel framework, called Disjoint Mapping Network (DIMNet), for
cross-modal biometric matching, in particular of voices and faces. Different
from the existing methods, DIMNet does not explicitly learn the joint
relationship between the modalities. Instead, DIMNet learns a shared
representation for different modalities by mapping them individually to their
common covariates. These shared representations can then be used to find the
correspondences between the modalities. We show empirically that DIMNet is able
to achieve better performance than other current methods, with the additional
benefits of being conceptually simpler and less data-intensive.
