Review of palm vein recognition

Wu, Wei; Elliott, Stephen J.; Lin, Sen; Sun, Shanhui; Tang, Yandong

doi:10.1049/iet-bmt.2019.0034

Cited by 70 publications

(36 citation statements)

References 104 publications

(155 reference statements)

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“…As to vein picture misshapenness, crafted by [50] to be featured, which acquaints another viewpoint that can assist to manage the issue of distortions for finger-vein biometric system. Their commitment is unique in relation to different works that attempt to lessen the impact of misshapenness by executing a more exact division of Return on investment (ROI), or utilizing a more powerful component extraction among others [28].The highlights got at the pixel level depend on the perception that the removal of pixels produced in the coordinating with measure, because of the misshapenness of vein pictures, are helpful as biased data.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Their technology is unique in relation to different works that attempt to lessen the impact of misshapenness by executing a more exact division of ROI or utilizing a more powerful component extraction among others. [28].The highlights got at the pixel level depend on the perception that the removal of pixels produced in the coordinating with measure, because of the misshapenness of vein pictures, are helpful as biased data. Edges and valleys with a high degree of distinctiveness are used in palm vein technology.…”

Section: ) 32 Study Populationsmentioning

confidence: 99%

“…[46]. [15], [19], [29], [31], [32], [33], [34], [35], [36], [37], [38], [39], [40], [41], [42], [43], [44], [46], [47], [48], [49], [50] IV. RESULTS…”

Section: D) Matchingmentioning

confidence: 99%

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A Framework for Verification in Contactless Secure Physical Access Control and Authentication Systems

Mwangi¹

2022

IJCIT

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Biometrics is one of the very popular techniques in user identification for accessing institutions and logging into attendance systems. Currently, some of the existing biometric techniques such as the use of fingerprints are unpopular due to COVID-19 challenges. This paper identifies the components of a framework for secure contactless access authentication. The researcher selected 50 journals from Google scholar which were used to analyze the various components used in a secure contactless access authentication framework. The methodology used for research was based on the scientific approach of research methodology that mainly includes data collection from the 50 selected journals, analysis of the data and assessment of results. The following components were identified: database, sensor camera, feature extraction methods, matching and decision algorithm. Out of the considered journals the most used is CASIA database at 40%, CCD Sensor camera with 56%, Gabor feature extraction method at 44%, Hamming distance for matching at 100% and PCA at 100% was used for decision making. These findings will assist the researcher in providing a guide on the best suitable components. Various researchers have proposed an improvement in the current security systems due to integrity and security problems.

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Section: Literature Reviewmentioning

confidence: 99%

Section: ) 32 Study Populationsmentioning

confidence: 99%

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A Framework for Verification in Contactless Secure Physical Access Control and Authentication Systems

Mwangi¹

2022

IJCIT

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“…Wu et al 31 have suggested a review of PVR. The palm vein was the body's internal biological information and vein patterns were difficult to copy for intruders likened to the different biometric characteristics.…”

Section: Literature Surveymentioning

confidence: 99%

A multimodal architecture using Adapt‐HKFCT segmentation and feature‐based chaos integrated deep neural networks (Chaos‐DNN‐SPOA) for contactless biometricpalm vein recognition system

Rajalakshmi

Annapurani

2021

Int J of Intelligent Sys

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In the recent past, the fusion of various unimodal biometrics has gained increasing attention from researchers dedicated to the use of practical biometrics. In this paper, a Chaos Integrated Deep Neural Networks (Chaos-DNN) using Sandpiper Optimization Algorithm (SPOA) is proposed to enhance the perfor- | INTRODUCTIONThe biometric system is used to measure the signature points of the individual persons, such as behaviour of a person's identity. [1][2][3] Biometric systems are used for security purposes to control the data or information for future use. It is also used for internet access, securing computer systems, electronic passport control security, banking, cellular phones, credit cards, security access to buildings, health and social services, terrorist determination and corpse recognition. [4][5][6] The biometric technologies are developed using human traits, namely, DNA, ear morphology, facial characteristics, fingerprint, gait, hand geometry, finger geometry, iris, keystroke, smell, palm print, handwriting with signature, voice and so forth. Among them palm venal traits are gaining more attention and are comparatively accurate and secure. 7,8 Multimodal systems are widely used in defence to secure the data compared with the unimodal systems. The imagery of vein is caught in various portions of the hand, like, palm, palm dorsal, the wrist, finger and so forth, which are widely used in recent years of evaluation. 9,10 The vein images, like palm and dorsal part are helps to build low-cost devices. Also the image capture method is more user-friendly, less harm, rapid, lack of contact and easy to use more secure for authentication systems. Hand-based parameters are widely used because of ease to work, availability of various parameters, like, geometrical features, hand thickness, fingers, joints and RAJALAKSHMI AND ANNAPURANI PANAIYAPPAN | 1847 How to cite this article: Rajalakshmi M, Annapurani Panaiyappan K. A multimodal architecture using Adapt-HKFCT segmentation and feature-based chaos integrated deep neural networks (Chaos-DNN-SPOA) for contactless biometricpalm vein recognition system.

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“…Outside box and contactless palm vein recognition in large-scale databases is the direction of this burgeoning re-search field [3]. Outside the NIR capture box makes the user feel comfortable but is affected by unstable and uneven visible light.…”

Section: Introductionmentioning

confidence: 99%

Outside Box and Contactless Palm Vein Recognition Based on a Wavelet Denoising ResNet

Wang

et al. 2021

IEEE Access

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Palm vein recognition is a high-security biometric. Outside the NIR capture box and contactless palm vein recognition are more popular but challenging. The users feel comfortable outside the NIR capture box but face more optical blurring brought by visible light. Contactless capture gestures solve the hygienic problem but face the image rotation, position translation, and scale transformation which makes classification difficult especially in large-scale databases. To address these problems, we develop a wavelet denoising ResNet, which consists of two models: the wavelet denoising (WD) model and the squeeze-and-excitation ResNet18 (SER) model. The WD model focuses on removing noise from skin scattering and optical blurring from palm vein images. The WD model enhances the low-frequency feature into a deep learning feature by residual learning technology. This strategy increases the weight of an effective handcrafted feature in the deep learning network. The SER model overcomes rotation, position translation, and scale transformation by selectively emphasizing classification features and weakening less useful features. To train and verify the network, an inside box palm vein image database and an outside box palm vein image database are set up. The Tongji contactless palm vein image database was also employed in the experiments. The validity and superiority of our network are verified in a series of experiments.

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Review of palm vein recognition

Cited by 70 publications

References 104 publications

A Framework for Verification in Contactless Secure Physical Access Control and Authentication Systems

A Framework for Verification in Contactless Secure Physical Access Control and Authentication Systems

A multimodal architecture using Adapt‐HKFCT segmentation and feature‐based chaos integrated deep neural networks (Chaos‐DNN‐SPOA) for contactless biometricpalm vein recognition system

Outside Box and Contactless Palm Vein Recognition Based on a Wavelet Denoising ResNet

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