A Systematic Review of Finger Vein Recognition Techniques

Shaheed, Kashif; Han-gang, Liu; Yang, Gongping; Qureshi, Imran; Gou, Jianping; Yin, Yilong

doi:10.3390/info9090213

Cited by 113 publications

(58 citation statements)

References 105 publications

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“…Super pixel-based finger vein recognition method was presented in [37] to improve the recognition performance.A finger vein recognition method was implemented in [38] by application of a personalized best bit map (PBBM).A finger vein recognition algorithm was designed in [39] with the help of gradient-correlation. A systematic review of different finger vein recognition techniques was analyzed in [40]. To diminishing the vanishing gradient problem , to update the weight on pruned cascade [42] and recurrent cascade neural learning [43] to reached the best accuracy on the image classification on Cifar-100 data set.…”

Section: Literature Surveymentioning

confidence: 99%

Delta Ruled Fully Recurrent Deep Learning for Finger-Vein Verification

Rakkimuthu¹,

DHARMALINGAM²

2019

IJITEE

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Finger-vein verification is a significant problem to be resolved in image processing because it provides high security in many practical applications. Few research works have been designed in conventional works using different machine learning techniques. However, the verification accuracy of existing algorithms was not sufficient. Also, the amount of time required for verifying the input finger vein image was more. In order to overcome such limitations, Delta Ruled Fully Recurrent Deep Learning (DRFRDL) technique is proposed. The DRFRDL technique comprises of three main layers namely input, hidden, output layer for accurate finger-vein authentication. The input layer in DRFRDL Technique takes a number of finger vein images as input and then sent it to the hidden layer. The designed DRFRDL technique used numbers of hidden layers in order to deeply examine the input finger vein images. The result of the hidden layer is feeding back into the network along with the inputs in order to find outs the vein features that exist in a given image. Followed by, the extracted vein features at hidden layers are transmitted to the output layer. In DRFRDL technique, output layer applies Gaussian activation function that calculates the features matching score via determining the association between extracted vein features and the vein features that are already stored in the database. After estimating the matching score, the output layer returns the verification result. If the output layer result is 1, then vein features are matched and the user is considered as authorized person. Otherwise, vein features are not matched and the user is considered as unauthorized person. Thus, DRFRDL technique increases the authentication performance of finger-vein with higher accuracy and minimal time. The simulation of DRFRDL Technique is conducted using metrics such as verification accuracy, verification time and false positive rate with respect to a different number of finger-vein images. The simulation results depict that the DRFRDL Technique is able to improve the accuracy and also reduces the amount of time needed for finger-vein verification when compared to state-of-the-art works.

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Section: Literature Surveymentioning

confidence: 99%

Delta Ruled Fully Recurrent Deep Learning for Finger-Vein Verification

Rakkimuthu¹,

DHARMALINGAM²

2019

IJITEE

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“…The most important characteristics of an object are taken as features and it should be extracted to provide a feature description of the object [24]. Shaheed, Kashif, et al, [14] in this paper, it represents some of the major steps of finger vein recognition. In this step, the quantifiable property of the basic biometric trait is created, which is known as template, this template is useful to identify a user.…”

Section: B Features Extractionmentioning

confidence: 99%

Palm Vein Recognition System for Human Authentication: A Review

Parihar¹

2019

IJRASET

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Palm vein technology is the upcoming technology that is extremely secure and unique because one cannot forged the pattern of veins. No doubt there are numerous biometric recognition techniques such as palm vein, iris, fingerprint, retina etc., which will be used as biometric identifiers to confirm the human's identity, but those methods didn't produce satisfactory results, so we need a technology that secure our information more efficiently from an unauthorized access. This paper presents an analysis of palm vein pattern recognition, its development, principle & general framework of palm and finger vein recognition, its techniques, its applications, and advantages of using this technology.

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“…Based on the criteria, there are several distinctive characteristics, or biometric traits, which have been studied and tested. Among them are: fingerprint [4,5], face [6,7], iris [8,9], palmprint [10,11], voice [12,13], finger and palm vein [14,15], gait [16,17], signature [18], DNA [19,20], and others. An extensive review of biometric technology is presented in [21].…”

Section: Introductionmentioning

confidence: 99%

Individuals Identification Based on Palm Vein Matching under a Parallel Environment

et al. 2019

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Biometric identification and verification are essential mechanisms in modern society. Palm vein recognition is an emerging biometric technique, which has several advantages, especially in terms of security against forgery. Contactless palm vein systems are more suitable for real-world applications, but two of the major challenges of the state-of-the-art contributions are image deformations and time efficiency. In the present work, we propose a new method for palm vein recognition by combining DAISY descriptor and the Coarse-to-fine PatchMatch (CPM) algorithm in a parallel matching process. Our proposal aims at providing an effective and efficient technique to obtain similarity of palm vein images considering their displacements as discriminatory information. Extensive evaluation on three publicly available databases demonstrates that the discriminability of the proposed approach reaches the state-of-the-art results while it is considerably superior in time efficiency.

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A Systematic Review of Finger Vein Recognition Techniques

Cited by 113 publications

References 105 publications

Delta Ruled Fully Recurrent Deep Learning for Finger-Vein Verification

Delta Ruled Fully Recurrent Deep Learning for Finger-Vein Verification

Palm Vein Recognition System for Human Authentication: A Review

Individuals Identification Based on Palm Vein Matching under a Parallel Environment

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