Synthesis of Multi-View 3D Fingerprints to Advance Contactless Fingerprint Identification

Billions of contact-based fingerprint images have been acquired in large databases. Contactless 2D fingerprint identification systems have emerged to provide more hygienic and secured alternatives and are highly sought under the current pandemic. The success of such an alternative requires high match accuracy, not just for the contactless-to-contactless but also for the contactless-to-contact-based matching, which is currently below expectations for large-scale deployments. We introduce a new approach to advance such expectations on match accuracy and also to address privacy-related concerns, e.g., recent GDPR regulations, in the acquisition of very large databases. This paper introduces a novel approach for accurately synthesizing multi-view contactless 3D fingerprints to develop a very large-scale multi-view fingerprint database, and corresponding contact-based fingerprint database. A unique advantage of our approach is the simultaneous availability of much-needed ground truth labels and alleviation of laborious and often prone to erroneous tasks performed by human labeling. We also introduce a new framework that can not only accurately match contactless to contact-based images but also contactless to contactless images, as both of these capabilities are simultaneously required to advance contactless fingerprint technologies. Our rigorous experimental results presented in this paper, both for within-database and cross-database experiments, illustrate outperforming results to simultaneously meet both of these expectations and validate the effectiveness of the proposed approach.

Automated summary

This paper introduces a new approach to accurately synthesize multi-view contactless 3D fingerprints and addresses the challenges of match accuracy and privacy concerns. The experimental results demonstrate the effectiveness of the proposed method.