Collaborative filtering model for enhancing fingerprint image

Bian, Weiwei; Ding, Shifei; Jia, Weikuan

doi:10.1049/iet-ipr.2017.0059

Cited by 13 publications

(4 citation statements)

References 22 publications

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“…This undoubtedly shrinks fingerprint image contrast space and weakens its contrast. In this research, we use the LCS [18] to enhance the contrast of fingerprint enhanced by Gabor filter [18]. As is shown in Fig.…”

Section: Fingerprint Image Pre-enhancementmentioning

confidence: 99%

“…So, it can ensure that the higher quality spectra diffuse into lower quality patches with the help of the scheme of patch quality grading. Bian et al [18] designed a collaborative filtering model for enhancing fingerprint image, where the Gabor filter and linear contrast stretching (LCS) are employed to preenhance the original fingerprint, and subsequently enhancing the pre-enhanced fingerprint using the collaborative model.…”

Section: Introductionmentioning

confidence: 99%

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Fingerprint enhancement using multi‐scale classification dictionaries with reduced dimensionality

Bian

Cheng

et al. 2020

IET biom.

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In order to improve the quality of fingerprint with a large noise, this study proposes a fingerprint enhancement method by using a sparse representation of learned multi-scale classification dictionaries with reduced dimensionality. The multi-scale dictionary is used to balance the contradiction between the accuracy and the anti-noise ability, which is an ideal solution to reconcile the demands of enhancement quality and computational performance. The principal component analysis is applied in the authors' 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 results in terms of the robustness of fingerprint enhancement as compared with the latest techniques. Moreover, the results show that the proposed algorithm can obtain better identification performance.

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Section: Fingerprint Image Pre-enhancementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fingerprint enhancement using multi‐scale classification dictionaries with reduced dimensionality

Bian

Cheng

et al. 2020

IET biom.

Self Cite

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“…The segmentation of single latent fingerprints is a challenging problem. There are several approaches for segmentation and enhancement of single latent fingerprints in the literature [1][2][3][4][5][6][7][8][9][10][23][24][25], some of them dealing with difficult databases, but none of them deals with segmentation of overlapped latent fingerprints, the special case where the overlapping fingerprint area should be distinct from single fingerprint area, in addition to distinguishing between fingerprint area and background.…”

Section: Problem Definitionmentioning

confidence: 99%

Deep learning‐based approach to latent overlapped fingerprints mask segmentation

Stojanović

Marques

Nešković

2018

IET Image Processing

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“…Although significant advances have been achieved for extracting fingerprint orientation field (FOF) in literatures, it is still challenging to reliably estimate the orientations of poor quality and latent fingerprints, which are usually caused by unclear ridge structure and various overlapping patterns. Due to its inherent global and reliable nature, the FOF plays a very important role in the areas of fingerprint segmentation [7]- [10], enhancement [11]- [20], singularity detection [21]- [25], classification [26]- [32], and matching [33]- [42]. Errors in computing the FOF propagate through all the stages of the AFIS.…”

Section: Introductionmentioning

confidence: 99%

A Survey of the Methods on Fingerprint Orientation Field Estimation

Bian

et al. 2019

IEEE Access

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Fingerprint orientation field (FOF) estimation plays a key role in enhancing the performance of the automated fingerprint identification system (AFIS): accurate estimation of FOF can evidently improve the performance of AFIS. However, despite the enormous attention on the FOF estimation research in the past decades, the accurate estimation of FOFs, especially for poor-quality fingerprints, still remains a challenging task. In this paper, we devote to review and categorization of the large number of FOF estimation methods proposed in the specialized literature, with particular attention to the most recent work in this area. Broadly speaking, the existing FOF estimation methods can be grouped into three categories: gradient-based methods, mathematical models-based methods, and learning-based methods. Identifying and explaining the advantages and limitations of these FOF estimation methods is of fundamental importance for fingerprint identification, because only a full understanding of the nature of these methods can shed light on the most essential issues for FOF estimation. In this paper, we make a comprehensive discussion and analysis of these methods concerning their advantages and limitations. We have also conducted experiments using publically available competition dataset to effectively compare the performance of the most relevant algorithms and methods.

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Collaborative filtering model for enhancing fingerprint image

Cited by 13 publications

References 22 publications

Fingerprint enhancement using multi‐scale classification dictionaries with reduced dimensionality

Fingerprint enhancement using multi‐scale classification dictionaries with reduced dimensionality

Deep learning‐based approach to latent overlapped fingerprints mask segmentation

A Survey of the Methods on Fingerprint Orientation Field Estimation

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