Enhancement-Driven Pretraining for Robust Fingerprint Representation Learning
This work addresses the need for reliable fingerprint recognition in biometric applications, but it appears incremental as it builds on existing U-Net-based enhancement methods.
The paper tackles the problem of robust fingerprint representation learning by proposing an enhancement-driven pretraining method, resulting in a marked improvement in verification performance compared to established self-supervised techniques on publicly available datasets.
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.