Zhuhai People's Hospital (Zhuhai Hospital Affiliated with Jinan University), No. 79, Kangning Road,Xiangzhou District, Zhuhai , 519000, Guangdong, China.
Harbin Institute of Technology Shenzhen, HIT Campus of University Town of Shenzhen, Shenzhen, 518055, Guangdong, China.
BMC Bioinformatics. 2020 Dec 28;21(Suppl 21):514. doi: 10.1186/s12859-020-03857-z.
Fingerprint biometrics play an essential role in authentication. It remains a challenge to match fingerprints with the minutiae or ridges missing. Many fingerprints failed to match their targets due to the incompleteness.
In this work, we modeled the fingerprints with Bezier curves and proposed a novel algorithm to detect and restore fragmented ridges in incomplete fingerprints. In the proposed model, the Bezier curves' control points represent the fingerprint fragments, reducing the data size by 89% compared to image representations. The representation is lossless as the restoration from the control points fully recovering the image. Our algorithm can effectively restore incomplete fingerprints. In the SFinGe synthetic dataset, the fingerprint image matching score increased by an average of 39.54%, the ERR (equal error rate) is 4.59%, and the FMR1000 (false match rate) is 2.83%, these are lower than 6.56% (ERR) and 5.93% (FMR1000) before restoration. In FVC2004 DB1 real fingerprint dataset, the average matching score increased by 13.22%. The ERR reduced from 8.46% before restoration to 7.23%, and the FMR1000 reduced from 20.58 to 18.01%. Moreover, We assessed the proposed algorithm against FDP-M-net and U-finger in SFinGe synthetic dataset, where FDP-M-net and U-finger are both convolutional neural network models. The results show that the average match score improvement ratio of FDP-M-net is 1.39%, U-finger is 14.62%, both of which are lower than 39.54%, yielded by our algorithm.
Experimental results show that the proposed algorithm can successfully repair and reconstruct ridges in single or multiple damaged regions of incomplete fingerprint images, and hence improve the accuracy of fingerprint matching.
指纹生物识别在身份验证中起着至关重要的作用。匹配缺失细节或脊线的指纹仍然是一个挑战。由于不完整，许多指纹未能与其目标匹配。
在这项工作中，我们使用 Bezier 曲线对指纹进行建模，并提出了一种新的算法来检测和恢复不完整指纹中的断裂脊线。在所提出的模型中，Bezier 曲线的控制点表示指纹碎片，与图像表示相比，数据大小减少了 89%。表示是无损的，因为从控制点完全恢复图像。我们的算法可以有效地恢复不完整的指纹。在 SFinGe 合成数据集上，指纹图像匹配得分平均提高了 39.54%，ERR（相等错误率）为 4.59%，FMR1000（假匹配率）为 2.83%，这些都低于修复前的 6.56%（ERR）和 5.93%（FMR1000）。在 FVC2004 DB1 真实指纹数据集上，平均匹配得分提高了 13.22%。ERR 从修复前的 8.46%降低到 7.23%，FMR1000 从 20.58 降低到 18.01%。此外，我们在 SFinGe 合成数据集上评估了针对 FDP-M-net 和 U-finger 的提议算法，FDP-M-net 和 U-finger 都是卷积神经网络模型。结果表明，FDP-M-net 的平均匹配得分提高率为 1.39%，U-finger 为 14.62%，均低于我们算法产生的 39.54%。
实验结果表明，所提出的算法可以成功修复和重建不完整指纹图像中单个或多个损坏区域的脊线，从而提高指纹匹配的准确性。
