Lin Chenhao, Kumar Ajay
IEEE Trans Image Process. 2018 Apr;27(4):2008-2021. doi: 10.1109/TIP.2017.2788866. Epub 2018 Jan 1.
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 contactbased 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.
为保护国家边境和支持电子政务项目，已开发出包含数十亿基于接触式指纹的庞大数据库。新兴的非接触式指纹传感器具有更好的卫生性、安全性和准确性。然而，此类非接触式指纹技术的采用/成功很大程度上取决于将非接触式二维指纹与传统基于接触式的指纹数据库进行匹配的先进能力。本文研究了此类问题，并开发了一种新方法来精确匹配此类指纹图像。开发了鲁棒薄板样条（RTPS），以使用样条更准确地对弹性指纹变形进行建模。为了校正基于接触式指纹的此类变形，提出了基于RTPS的广义指纹变形校正模型（DCM）。DCM的使用使得在非接触式和基于接触式的指纹上观察到的关键细节特征能够精确对齐。通过纳入与细节相关的纹路，研究了这种交叉匹配性能的进一步改进。我们还开发了一个新的数据库，包含从300个客户获取的1800个非接触式二维指纹及其相应的基于接触式的指纹，并将其公开以供进一步研究。本文使用两个公开可用数据库给出的实验结果验证了我们的方法，并在匹配非接触式二维和基于接触式的指纹图像方面取得了优异的结果。
