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Fingerprint enhancement using multi-scale classification dictionaries with reduced dimensionality

Bian, Weixin; Xu, Deqin; Cheng, Yongqiang; Li, Qingde; Luo, Yonglong; Yu, Qingying

Authors

Weixin Bian

Deqin Xu

Yonglong Luo

Qingying Yu



Abstract

In order to improve the quality of fingerprint with large noise, this paper proposes a fingerprint enhancement method by using a sparse representation of learned multi-scale classification dictionaries with reduced dimensionality. Multi-scale dictionary is used to balance the contradiction between the accuracy and the anti-noise ability, which has been shown to be an ideal solution to reconcile the demands of enhancement quality and computational performance. Principal component analysis (PCA)is applied in our technique for dimension reduction of multi-scale classification dictionaries. Under the quality grading scheme and multi-scale composite windows, the fingerprint patches are enhanced by using a sparse representation of learned multi-scale classification dictionaries with reduced dimensionality according to their priorities. In addition, the multi-scale composite windows help the more high quality spectra diffuse into the low quality fingerprint patches and this can greatly improve the spectra quality of them. Experimental results and comparisons on FVC 2000 and FVC 2004 databases are reported.And it shows that the proposed method yields better result in terms of the robustness of fingerprint enhancement as compared with latest techniques.Moreover, the results show that the proposed algorithm can obtain better identification performance

Journal Article Type Article
Publication Date Sep 1, 2020
Journal IET Biometrics
Print ISSN 2047-4938
Electronic ISSN 2047-4946
Publisher Institution of Engineering and Technology
Peer Reviewed Peer Reviewed
Volume 9
Issue 5
Pages 194-204
APA6 Citation Bian, W., Xu, D., Cheng, Y., Li, Q., Luo, Y., & Yu, Q. (2020). Fingerprint enhancement using multi-scale classification dictionaries with reduced dimensionality. IET Biometrics, 9(5), 194-204. https://doi.org/10.1049/iet-bmt.2019.0121
DOI https://doi.org/10.1049/iet-bmt.2019.0121
Keywords Signal Processing; Software; Computer Vision and Pattern Recognition
Publisher URL https://digital-library.theiet.org/content/journals/10.1049/iet-bmt.2019.0121
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