提出多阶段对比训练方法，提升非接触指纹识别精度
Ridgeformer: Mutli-Stage Contrastive Training For Fine-grained Cross-Domain Fingerprint Recognition
- 分阶段提取全局与局部特征，增强跨样本对齐
- 在HKPolyU和RidgeBase数据集上超越现有方法
- 适合需要高精度非接触生物识别的场景
日益增长的无接触生物识别需求凸显了非接触指纹识别技术的重要性。尽管潜力巨大，该技术仍面临成像模糊、纹线与谷地对比度低、手指位置变化及视角畸变等挑战，严重影响匹配准确性和可靠性。为此，我们提出一种基于Transformer的多阶段非接触指纹匹配方法，先捕捉全局空间特征，再细化局部特征对齐。通过分层特征提取与匹配流程，确保细粒度跨样本对齐并保持全局特征鲁棒性。我们在公开数据集HKPolyU和RidgeBase上进行了广泛评估，涵盖非接触对接触、非接触对非接触等多种匹配协议，结果表明所提方法优于现有技术，包括商用方案。
原文摘要 · Abstract (English)
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.
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