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Deep Coupled GAN-Based Score-Level Fusion for Multi-Finger Contact to Contactless Fingerprint Matching
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Datum
2022
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Gesellschaft für Informatik e.V.
Zusammenfassung
Interoperability between contact to contactless images in fingerprint matching is a key
factor in the success of contactless fingerprinting devices, which have recently witnessed an increasing
demand for biometric authentication. However, due to the presence of perspective distortion
and the absence of elastic deformation in contactless fingerphotos, direct matching between contactless
fingerprint probe images and legacy contact-based gallery images produces a low accuracy. In
this paper, to improve interoperability, we propose a coupled deep learning framework that consists
of two Conditional Generative Adversarial Networks. Generative modeling is employed to find a
projection that maximizes the pairwise correlation between these two domains in a common latent
embedding subspace. Extensive experiments on three challenging datasets demonstrate significant
performance improvements over the state-of-the-art methods and two top-performing commercial
off-the-shelf SDKs, i.e., Verifinger 12.0 and Innovatrics. We also achieve a high-performance gain
by combining multiple fingers of the same subject using a score fusion model.