  Fingerprint recognition stands as a pivotal component of biometric
technology, with diverse applications from identity verification to advanced
search tools. In this paper, we propose a unique method for deriving robust
fingerprint representations by leveraging enhancement-based pre-training.
Building on the achievements of U-Net-based fingerprint enhancement, our method
employs a specialized encoder to derive representations from fingerprint images
in a self-supervised manner. We further refine these representations, aiming to
enhance the verification capabilities. Our experimental results, tested on
publicly available fingerprint datasets, reveal a marked improvement in
verification performance against established self-supervised training
techniques. Our findings not only highlight the effectiveness of our method but
also pave the way for potential advancements. Crucially, our research indicates
that it is feasible to extract meaningful fingerprint representations from
degraded images without relying on enhanced samples.
