Parallel Prints: Generating Realistic Cancelable Fingerprint Templates

Fingerprints are widely recognized as a crucial method for uniquely identifying individuals. Nevertheless, the utilization of fingerprints in an online platform presents a noteworthy concern for privacy, as the original fingerprints are constantly vulnerable to various kinds of attacks, thefts, forgery, unintended storage, and processing by third parties. The use of original fingerprints for authentication is riddled with concerns surrounding privacy and security. To address this, we introduce artificial intelligence techniques to transform the original unique fingerprint into a reliable natural looking template that can be stored and used for authentication so that the need for decoding at the end of a third party is eliminated. The study proposes a novel approach to creating extremely realistic fingerprint templates while also providing the ability to revoke and cancel stolen fingerprints in the event of an attack. We used the various publicly available datasets during the training and testing phases. We tested the quality of generated data throughout the testing phase, where the system performance is reported at EER of 0.06% and AUC of 0.99.