Fingerprint enhancement using multi-scale classification dictionaries with reduced dimensionality

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

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

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

• Journal Article Type: Article
• Acceptance Date: May 7, 2020
• Online Publication Date: May 11, 2020
• Publication Date: Sep 1, 2020
• Deposit Date: Jul 20, 2020
• Publicly Available Date: May 12, 2021
• Journal: IET Biometrics
• Print ISSN: 2047-4938
• Publisher: Institution of Engineering and Technology (IET)
• Peer Reviewed: Peer Reviewed
• Volume: 9
• Issue: 5
• Pages: 194-204
• DOI: https://doi.org/10.1049/iet-bmt.2019.0121
• Keywords: Signal Processing; Software; Computer Vision and Pattern Recognition
• Public URL: https://hull-repository.worktribe.com/output/3546347
• Publisher URL: https://digital-library.theiet.org/content/journals/10.1049/iet-bmt.2019.0121