Matching Contactless and Contact-Based Conventional Fingerprint Images for Biometrics Identification
Matching Contactless and Contact-Based Conventional Fingerprint Images for Biometrics Identification
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DOI：
10.1109/tip.2017.2788866
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发表时间：
2018-04-01
影响因子：
10.6
通讯作者：
Kumar, Ajay
中科院分区：
文献类型：
--
作者：
Lin, Chenhao;Kumar, Ajay
为了保护国家边界和支持电子政务项目，已经开发了数十亿基于接触的指纹的庞大数据库。新兴的非接触式指纹传感器提供了更好的卫生、安全性和准确性。然而，这种非接触式指纹技术的采用/成功在很大程度上取决于将非接触式二维指纹与传统的接触式指纹数据库相匹配的先进能力。本文对这一问题进行了研究，提出了一种准确匹配指纹图像的新方法。为了更精确地模拟指纹弹性变形，提出了一种基于薄板样条的鲁棒模型。为了校正基于接触指纹的变形，提出了基于rtps的广义指纹变形校正模型（DCM）。DCM的使用导致在非接触和基于接触的指纹上观察到的关键细节特征的精确对齐。进一步改进这种交叉匹配性能的研究纳入细节相关脊。我们还开发了一个新的数据库，该数据库由来自300个客户的1800个非接触式二维指纹和相应的接触式指纹组成，并向公众开放，以供进一步研究。本文中提出的实验结果，使用两个公开可用的数据库，验证了我们的方法，并在匹配非接触式二维和基于接触的指纹图像方面取得了优异的结果。
Vast databases of billions of contact-based fingerprints have been developed to protect national borders and support e-governance programs. Emerging contactless fingerprint sensors offer better hygiene, security, and accuracy. However, the adoption/success of such contactless fingerprint technologies largely depends on advanced capability to match contactless 2D fingerprints with legacy contact-based fingerprint databases. This paper investigates such problem and develops a new approach to accurately match such fingerprint images. Robust thin-plate spline (RTPS) is developed to more accurately model elastic fingerprint deformations using splines. In order to correct such deformations on the contact-based fingerprints, RTPS-based generalized fingerprint deformation correction model (DCM) is proposed. The usage of DCM results in accurate alignment of key minutiae features observed on the contactless and contact-based fingerprints. Further improvement in such cross-matching performance is investigated by incorporating minutiae related ridges. We also develop a new database of 1800 contactless 2D fingerprints and the corresponding contact-based fingerprints acquired from 300 clients which is made publicly accessible for further research. The experimental results presented in this paper, using two publicly available databases, validate our approach and achieve outperforming results for matching contactless 2D and contact-based fingerprint images.