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We propose a novel framework, called Disjoint Mapping Network (DIMNet), forcross-modal biometric matching, in particular of voices and faces. Differentfrom the existing methods, DIMNet does not explicitly learn the jointrelationship between the modalities. Instead, DIMNet learns a sharedrepresentation for different modalities by mapping them individually to theircommon covariates. These shared representations can then be used to find thecorrespondences between the modalities. We show empirically that DIMNet is ableto achieve better performance than other current methods, with the additionalbenefits of being conceptually simpler and less data-intensive.

Disjoint Mapping Network for Cross-modal Matching of Voices and Faces