On improving interoperability for cross-domain multi-finger fingerprint matching using coupled adversarial learning

Improving interoperability in contactless-to-contact fingerprint matching is a crucial factor for the mainstream adoption of contactless fingerphoto devices. However, matching contactless probe images against legacy contact-based gallery images is very challenging due to the presence of heterogeneity between these domains. Moreover, unconstrained acquisition of fingerphotos produces perspective distortion. Therefore, direct matching of fingerprint features suffers severe performance degradation on cross-domain interoperability. In this study, to address this issue, the authors propose a coupled adversarial learning framework to learn a fingerprint representation in a low-dimensional subspace that is discriminative and domain-invariant in nature. In fact, using a conditional coupled generative adversarial network, the authors project both the contactless and the contact-based fingerprint into a latent subspace to explore the hidden relationship between them using class-specific contrastive loss and ArcFace loss. The ArcFace loss ensures intra-class compactness and inter-class separability, whereas the contrastive loss minimises the distance between the subspaces for the same finger. Experiments on four challenging datasets demonstrate that our proposed model outperforms state-of-the methods and two top-performing commercial-off-the-shelf SDKs, that is, Verifinger v12.0 and Innovatrics. In addition, the authors also introduce a multi-finger score fusion network that significantly boosts interoperability by effectively utilising the multi-finger input of the same subject for both cross-domain and cross-sensor settings.