Fingerprint restoration using cubic Bezier curve

Fingerprint restoration using cubic Bezier curve

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 no...

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Bibliographic Details
Published in:BMC bioinformatics Vol. 21; no. Suppl 21; p. 514
Main Authors: Tu, Yanglin, Yao, Zengwei, Xu, Jiao, Liu, Yilin, Zhang, Zhe
Format: Journal Article
Language:English
Published: England BioMed Central Ltd 28-12-2020
BioMed Central
BMC
Subjects:
Accuracy
Algorithms
Analysis
Artificial neural networks
Bezier curve
Biometric Identification - methods
Biometrics
Breakpoints
Curves
Datasets
Dermatoglyphics
Evaluation
Finger
Fingerprint description
Fingerprinting
Fingerprints
Humans
Image reconstruction
Image restoration
Incomplete fingerprint
Matching
Neural networks
Parametric devices
Reconstruction
Representations
Ridges
China
Reconstruction
Breakpoints
Fingerprint description
Bezier curve
Incomplete fingerprint
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Summary: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.
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ISSN:1471-2105
1471-2105
DOI:10.1186/s12859-020-03857-z