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.

翻译：指纹识别作为生物识别技术的关键组成部分，在身份验证到高级搜索工具等众多领域有广泛应用。本文提出一种独特方法，通过基于增强的预训练技术获取鲁棒指纹表示。基于U-Net指纹增强方法的既有成果，我们的方法采用专用编码器以自监督方式从指纹图像中提取表示，并进一步优化这些表示以增强验证能力。在公开指纹数据集上的实验结果显示，相较于现有自监督训练技术，本方法在验证性能上取得显著提升。该发现不仅验证了方法的有效性，更开辟了潜在的技术进展方向。关键性突破在于，研究表明无需依赖增强样本即可从退化图像中提取有意义的指纹表示。