标题： Ridgeformer：细粒度跨域指纹识别的多阶段对比训练 显示英文标题

标题： Ridgeformer: Mutli-Stage Contrastive Training For Fine-grained Cross-Domain Fingerprint Recognition

摘要： 对卫生和便携式生物识别系统日益增长的需求凸显了非接触式指纹识别技术进步的紧迫性。 尽管这项技术具有潜力，但它也面临显著挑战，包括图像聚焦不良、指纹脊与谷之间的对比度降低、手指定位变化以及透视失真等问题。 这些因素极大地影响了非接触式指纹匹配的准确性和可靠性。 为了解决这些问题，我们提出了一种基于多阶段变换器的新型非接触式指纹匹配方法，该方法首先捕获全局空间特征，然后细化指纹样本之间的局部特征对齐。 通过采用分层特征提取和匹配管道，我们的方法确保了精细的跨样本对齐，同时保持了全局特征表示的鲁棒性。 我们在公开数据集（如HKPolyU和RidgeBase）上进行了广泛的评估，使用不同的评估协议，如非接触式到接触式匹配和非接触式到非接触式匹配，并证明我们的方法优于现有方法，包括商用现货解决方案。 显示英文摘要

摘要： The increasing demand for hygienic and portable biometric systems has underscored the critical need for advancements in contactless fingerprint recognition. Despite its potential, this technology faces notable challenges, including out-of-focus image acquisition, reduced contrast between fingerprint ridges and valleys, variations in finger positioning, and perspective distortion. These factors significantly hinder the accuracy and reliability of contactless fingerprint matching. To address these issues, we propose a novel multi-stage transformer-based contactless fingerprint matching approach that first captures global spatial features and subsequently refines localized feature alignment across fingerprint samples. By employing a hierarchical feature extraction and matching pipeline, our method ensures fine-grained, cross-sample alignment while maintaining the robustness of global feature representation. We perform extensive evaluations on publicly available datasets such as HKPolyU and RidgeBase under different evaluation protocols, such as contactless-to-contact matching and contactless-to-contactless matching and demonstrate that our proposed approach outperforms existing methods, including COTS solutions.