Finger vein recognition based on lightweight CNN combining center loss and dynamic regularization

Zhao, Dongdong; Ma, Hui; Yang, Zhengeng; Li, Jianian; Tian, Wenbo

doi:10.1016/j.infrared.2020.103221

Cited by 56 publications

(40 citation statements)

References 14 publications

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“…The simulation process, the proposed technique and state-of-the-arts LC-CNN [1], Merge CNN [2], CNN [3], FV-GAN [4], and WLD [5] are implemented with help of MATLAB 2019B simulator coding. The varied number of finger vein images is taken from the input dataset [38] during the simulation evaluation for accurate verification.…”

Section: Experimental Scenariomentioning

confidence: 99%

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Deep Belief Structure Learning for Finger Vein Verification by Solving the Biometric Recognition Model

Muthusamy¹,

Ponnusamy²

2022

Preprint

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Vein authentication is a novel biometric method to authenticate the individuality of a person. The conventional biometric technique employs shape images and exact segments of finger veins for the verification process. To improve the verification accuracy, a novel Anisotropic Filtered Stromberg Feature Transform based on Tucker’s Congruence Deep Belief Structure Learning (AFSFT-TCDBSL) technique is intended. The proposed AFSFT-TCDBSL technique comprises one input, three hidden, and one output layers. The numbers of images are collected in the input, and input images are preprocessed using anisotropic diffusion filtering in the first hidden layer. Finally, the verification process is performed using Tucker’s congruence correlation coefficient (TCCC). Based on the correlation, the verification outputs are getting to the output layer. In this way, accurate finger vein verification is performed with superior accuracy and with a minimum false rate. We performed experimental assessments with different factors, such as PSNR, FVVA, FPR, and CT. The proposed ADFSFT-TCDBSL technique offers better finger-vein verification results than the state-of-the-art methods.

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Section: Experimental Scenariomentioning

confidence: 99%

“…However, it failed to perform the correct feature matching. To extract the vein patterns, an innovative technique of finger vein verification based on generative adversarial networks was developed [4]. However, the performance of finger vein verification was not enhanced by using a larger database.…”

Section: Introductionmentioning

confidence: 99%

Deep Belief Structure Learning for Finger Vein Verification by Solving the Biometric Recognition Model

Muthusamy¹,

Ponnusamy²

2022

Preprint

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“…Lastly, the two scores are fused together to provide the final decision. Cherrat et al [ 127 ], in their finger vein system, used a CNN as a feature extractor combined with a Random Forest model for the classification, while Zhao et al [ 128 ] used a lightweight CNN for the classification and focused on the loss function by using the center loss function and dynamic regularization. Hao et al [ 129 ] proposed a multi-tasking neural network that performs both ROI and feature extraction sequentially, through two branches.…”

Section: Feature Extraction Vs Feature Learningmentioning

confidence: 99%

Feature Extraction for Finger-Vein-Based Identity Recognition

et al. 2021

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This paper aims to provide a brief review of the feature extraction methods applied for finger vein recognition. The presented study is designed in a systematic way in order to bring light to the scientific interest for biometric systems based on finger vein biometric features. The analysis spans over a period of 13 years (from 2008 to 2020). The examined feature extraction algorithms are clustered into five categories and are presented in a qualitative manner by focusing mainly on the techniques applied to represent the features of the finger veins that uniquely prove a human’s identity. In addition, the case of non-handcrafted features learned in a deep learning framework is also examined. The conducted literature analysis revealed the increased interest in finger vein biometric systems as well as the high diversity of different feature extraction methods proposed over the past several years. However, last year this interest shifted to the application of Convolutional Neural Networks following the general trend of applying deep learning models in a range of disciplines. Finally, yet importantly, this work highlights the limitations of the existing feature extraction methods and describes the research actions needed to face the identified challenges.

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“…Another Artificial Intelligence-based approach was described in [5]. In this case the Authors proposed to reduce the time needed for human identity recognition based on finger veins by preparation of lightweight convolutional neural network model.…”

Section: Related Workmentioning

confidence: 99%

Raspberry Pi-Based Device for Finger Veins Collection and the Image Processing-Based Method for Minutiae Extraction

Szymkowski

2021

Computer Information Systems and Industrial Management

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Biometrics is one of the most important ways to secure users' data. It gained popularity due to effectiveness and ease of usage. It was proven that diversified solutions based on measurable traits can guarantee higher security levels than traditional authentication-based (logins and passwords). The most popular are fingerprint and iris (especially in mobile devices). In this work we would like to present our own algorithm connected with finger veins features extraction. At the beginning all details of the device for samples collection are given. In the further part significant information related to finger veins extraction are described in the details. Image processing methods were used to show that even with traditional, well-known algorithms it is possible to obtain precise information about human veins. The final step in our algorithm is connected with feature vector generation. In this work we do not present a classification stage as it is out of its scope.

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Finger vein recognition based on lightweight CNN combining center loss and dynamic regularization

Cited by 56 publications

References 14 publications

Deep Belief Structure Learning for Finger Vein Verification by Solving the Biometric Recognition Model

Deep Belief Structure Learning for Finger Vein Verification by Solving the Biometric Recognition Model

Feature Extraction for Finger-Vein-Based Identity Recognition

Raspberry Pi-Based Device for Finger Veins Collection and the Image Processing-Based Method for Minutiae Extraction

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