XFinger-Net: Pixel-Wise Segmentation Method for Partially Defective Fingerprint Based on Attention Gates and U-Net.

Guo Chun Wan, Meng Meng Li, He Xu, Wen Hao Kang, Jin Wen Rui, Mei Song Tong

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Guo Chun WanDepartment of Electronic Science and Technology, Tongji University, Shanghai 200092, China.

Meng Meng LiDepartment of Electronic Science and Technology, Tongji University, Shanghai 200092, China.ORCID 0000-0002-0620-8852

He XuDepartment of Electronic Science and Technology, Tongji University, Shanghai 200092, China.

Wen Hao KangDepartment of Electronic Science and Technology, Tongji University, Shanghai 200092, China.

Jin Wen RuiSino-German College of Applied Science, Tongji University, Shanghai 200092, China.

Mei Song TongDepartment of Electronic Science and Technology, Tongji University, Shanghai 200092, China.

Funding

National Natural Science Foundation of China 61671328

6 · The paper itself

Abstract

Partially defective fingerprint image (PDFI) with poor performance poses challenges to the automated fingerprint identification system (AFIS). To improve the quality and the performance rate of PDFI, it is essential to use accurate segmentation. Currently, most fingerprint image segmentations use methods with ridge orientation, ridge frequency, coherence, variance, local gradient, etc. This paper proposes a method of XFinger-Net for segmenting PDFIs. Based on U-Net, XFinger-Net inherits its characteristics. The attention gate with fewer parameters is used to replace the cascaded network, which can suppress uncorrelated regions of PDFIs. Moreover, the XFinger-Net implements a pixel-level segmentation and takes non-blocking fingerprint images as an input to preserve the global characteristics of PDFIs. The XFinger-Net can achieve a very good segmentation effect as demonstrated in the self-made fingerprint segmentation test.

Indexed as

DermatoglyphicsImage Processing, Computer-AssistedHumansattention gatesdeep learningfingerprint segmentationpartial defectU-Net

Identifiers

PMID32785159

DOI10.3390/s20164473

PMCPMC7474422

What OpenQuestion holds

Textmetadata

LicenceCC BY

Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.