Evidence map›Paper›PMID 33036259›Full record

ArticleSensors (Basel, Switzerland)2020

Contact-Free Multispectral Identity Verification System Using Palm Veins and Deep Neural Network.

Maciej Stanuch, Marek Wodzinski, Andrzej Skalski

Open access · goldAbstract 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. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.1field-weighted citation impact, top 23% of its field
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

4 citing papers in PubMed, 11 citations in OpenAlex.

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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

3 authors at 1 institution in 1 country.

Maciej StanuchDepartment of Measurement and Electronics, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Krakow, Poland.ORCID 0000-0002-6764-3815
Marek WodzinskiDepartment of Measurement and Electronics, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Krakow, Poland.ORCID 0000-0002-8076-6246
Andrzej SkalskiDepartment of Measurement and Electronics, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Krakow, Poland.ORCID 0000-0003-2299-458X
AGH University of Krakow · PL

Funding

Akademia Górniczo-Hutnicza im. Stanislawa Staszica Dean's Grant 2020Ministry of Science and Higher Education in Poland Statutory Activity
6 · The paper itself

Abstract

Devices and systems secured by biometric factors became a part of our lives because they are convenient, easy to use, reliable, and secure. They use information about unique features of our bodies in order to authenticate a user. It is possible to enhance the security of these devices by adding supplementary modality while keeping the user experience at the same level. Palm vein systems are based on infrared wavelengths used for capturing images of users' veins. It is both convenient for the user, and it is one of the most secure biometric solutions. The proposed system uses IR and UV wavelengths; the images are then processed by a deep convolutional neural network for extraction of biometric features and authentication of users. We tested the system in a verification scenario that consisted of checking if the images collected from the user contained the same biometric features as those in the database. The True Positive Rate (TPR) achieved by the system when the information from the two modalities were combined was 99.5% by the threshold of acceptance set to the Equal Error Rate (EER).

Indexed as

Biometric IdentificationNeural Networks, ComputerBiometryDatabases, FactualHandHumansVeinsbiometricsconvolutional neural networksmultimodalitypalm vein scanner

Identifiers

PMID33036259
PMCPMC7582870
OpenAlexW3092562770

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.