An available database for the research of finger vein recognition

Lu, Yu; Xie, Shan Juan; Yoon, Sook; Wang, Zhihui; Park, Dong Sun

doi:10.1109/cisp.2013.6744030

Cited by 147 publications

(60 citation statements)

References 7 publications

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“…There are many open finger vein databases such as SDUMLA-HMT [13], HKPU-FV [14], UTFV [15], MMCBNU_6000 [16], THU-FV [17], FV-USM [18] University developed their finger vein database called (HKPU-FV) [14], which consists of finger vein and low texture images. In 2010, Shandong University released one multimodal trait database SDUMLA-FV [13].…”

Section: Methodsmentioning

confidence: 99%

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A Systematic Review of Finger Vein Recognition Techniques

Han-gang²,

et al. 2018

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Biometric identification is the study of physiological and behavioral attributes of an individual to overcome security problems. Finger vein recognition is a biometric technique used to analyze finger vein patterns of persons for proper authentication. This paper presents a detailed review on finger vein recognition algorithms. Such tools include image acquisition, preprocessing, feature extraction and matching methods to extract and analyze object patterns. In addition, we list some novel findings after the critical comparative analysis of the highlighted techniques. The comparative studies indicate that the accuracy of finger vein identification methods is up to the mark.

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Section: Methodsmentioning

confidence: 99%

“…The third database UTFV [15] is presented by University of Twente. In the recent past, two finger vein databases, THU-FV [17] and MMCBU_6000 [16], were published by Tsinghua and Chunbuk Nation University respectively. All these public databases provide more than 100 subjects of finger veins, except UTFV database which provides 60 subjects.…”

Section: Methodsmentioning

confidence: 99%

A Systematic Review of Finger Vein Recognition Techniques

Han-gang²,

et al. 2018

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“…In the proposed work, the finger vein images are taken from MMCBNU_600 database [11] and the finger knuckle images are from Poly U FKP database.…”

Section: ) Data Collection Phasementioning

confidence: 99%

Secure Transmission of Medical Images Using Elliptic Curve Cryptography with Multimodal Biometric Authentication

Batmavady¹

2018

IJRASET

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The fastest growing trend in today's digital world is the use of telemedicine by health care professionals incorporating the latest technology to access, aid, decide, diagnose and treat the patients irrespective of the distance barriers. This in particular would help the rural communities where the immediate access to medical facilities are not available. Since, the information transmitted about the patients is to be maintained confidential, images and other data about the patients need to be encrypted to prevent hacking. To ensure the authentication of the user to access the database, password authentication followed by multimodal biometric fusion using finger vein and finger knuckle are incorporated in the proposed work. Further, authentication is strengthened by encrypting the fused image. The medical images are encrypted using Elliptic Curve Cryptographic (ECC) technique. Once the user is authenticated, he is allowed to access the medical image database

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“…Nevertheless, only a few publicly established benchmarks and data sets are available in this area [2, 1,6], and seldom does any evaluation system exist. Xian et al have developed an automated performance evaluation system RATE (Recognition Algorithm Test Engine) for finger vein recognition [3], and hosted PKU Finger Vein Recognition (PFVR) in the 9th Chinese Conference on Biometrics Recognition in 2014 [7].…”

Section: Introductionmentioning

confidence: 99%

The ICB-2015 Competition on Finger Vein Recognition

Xian

2015

2015 International Conference on Biometrics (ICB)

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Finger vein recognition is a newly developed and promising biometrics technology. To facilitate evaluation in this area and study state-of-the-art performance of the finger vein recognition algorithms, we organized The ICB-2015 Competition on Finger Vein Recognition (ICFVR2015). This competition is held on a general recognition algorithm evaluation platform called RATE, with 3 data sets collected from volunteers and actual usage. 7 algorithms were finally submitted, with the best EER achieving 0.375%. This paper will first introduce the organization of the competition and RATE, then describe data sets and test protocols, and finally present results of the competition.

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An available database for the research of finger vein recognition

Cited by 147 publications

References 7 publications

A Systematic Review of Finger Vein Recognition Techniques

A Systematic Review of Finger Vein Recognition Techniques

Secure Transmission of Medical Images Using Elliptic Curve Cryptography with Multimodal Biometric Authentication

The ICB-2015 Competition on Finger Vein Recognition

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