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.

中文翻译：

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使用三次贝塞尔曲线进行指纹恢复

指纹生物识别技术在身份验证中起着至关重要的作用。将指纹与细节或隆起进行匹配仍然是一个挑战。由于不完整，许多指纹无法匹配其目标。在这项工作中，我们使用Bezier曲线对指纹建模，并提出了一种新算法来检测和恢复不完整指纹中的碎片脊。在提出的模型中，贝塞尔曲线的控制点代表指纹片段，与图像表示相比，数据量减少了89％。由于控制点的还原完全恢复了图像，因此表示形式是无损的。我们的算法可以有效地还原不完整的指纹。在SFinGe合成数据集中，指纹图像匹配得分平均提高了39.54％，ERR（平均错误率）为4.59％，并且FMR1000（错误匹配率）为2.83％，低于恢复前的6.56％（ERR）和5.93％（FMR1000）。在FVC2004 DB1真实指纹数据集中，平均匹配得分提高了13.22％。ERR从恢复前的8.46％降低到7.23％，FMR1000从恢复前的20.58降低到18.01％。此外，我们在SFinGe合成数据集中针对FDP-M-net和U-finger评估了所提出的算法，其中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39.54％。实验结果表明，该算法可以成功修复和重建不完整指纹图像单个或多个受损区域的脊，