Contactless Multispectral Palm-Vein Recognition With Lightweight Convolutional Neural Network

The development of information technology has made it possible to replace traditional keys and passwords with biometric recognition. Among the various human recognition technologies, contactless palm-vein authentication is becoming increasingly popular because it is hygienic and safe. In the field o...

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Autores principales:	Yung-Yao Chen, Chih-Hsien Hsia, Ping-Han Chen
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Biometric palm-vein recognition convolutional neural networks triplet loss function handcrafted features Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/12db00b71822455498b214fba8d9d4e6
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id	oai:doaj.org-article:12db00b71822455498b214fba8d9d4e6
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spelling	oai:doaj.org-article:12db00b71822455498b214fba8d9d4e62021-11-18T00:02:38ZContactless Multispectral Palm-Vein Recognition With Lightweight Convolutional Neural Network2169-353610.1109/ACCESS.2021.3124631https://doaj.org/article/12db00b71822455498b214fba8d9d4e62021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9597538/https://doaj.org/toc/2169-3536The development of information technology has made it possible to replace traditional keys and passwords with biometric recognition. Among the various human recognition technologies, contactless palm-vein authentication is becoming increasingly popular because it is hygienic and safe. In the field of deep learning (DL), system security and multispectral compatibility are crucial issues that require outright solutions. One of the most widely investigated DL algorithms is the convolutional neural network (CNN), which has been proven to have strong feature extraction capability. However, the training of CNN requires large samples and thus entails a heavy computational load, resulting in high hardware and software costs. Therefore, this paper proposes an adaptive Gabor filter with enhanced imaging features and triplet loss function that captures sufficient palm-vein data. A multispectral palm database from the CASIA public database was employed in this study to analyze the proposed system. The experimental results show that the proposed method has a low recognition error rate of 0.0556% and uses only a few network parameters in a multispectral environment.Yung-Yao ChenChih-Hsien HsiaPing-Han ChenIEEEarticleBiometricpalm-vein recognitionconvolutional neural networkstriplet loss functionhandcrafted featuresElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 149796-149806 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biometric palm-vein recognition convolutional neural networks triplet loss function handcrafted features Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Biometric palm-vein recognition convolutional neural networks triplet loss function handcrafted features Electrical engineering. Electronics. Nuclear engineering TK1-9971 Yung-Yao Chen Chih-Hsien Hsia Ping-Han Chen Contactless Multispectral Palm-Vein Recognition With Lightweight Convolutional Neural Network
description	The development of information technology has made it possible to replace traditional keys and passwords with biometric recognition. Among the various human recognition technologies, contactless palm-vein authentication is becoming increasingly popular because it is hygienic and safe. In the field of deep learning (DL), system security and multispectral compatibility are crucial issues that require outright solutions. One of the most widely investigated DL algorithms is the convolutional neural network (CNN), which has been proven to have strong feature extraction capability. However, the training of CNN requires large samples and thus entails a heavy computational load, resulting in high hardware and software costs. Therefore, this paper proposes an adaptive Gabor filter with enhanced imaging features and triplet loss function that captures sufficient palm-vein data. A multispectral palm database from the CASIA public database was employed in this study to analyze the proposed system. The experimental results show that the proposed method has a low recognition error rate of 0.0556% and uses only a few network parameters in a multispectral environment.
format	article
author	Yung-Yao Chen Chih-Hsien Hsia Ping-Han Chen
author_facet	Yung-Yao Chen Chih-Hsien Hsia Ping-Han Chen
author_sort	Yung-Yao Chen
title	Contactless Multispectral Palm-Vein Recognition With Lightweight Convolutional Neural Network
title_short	Contactless Multispectral Palm-Vein Recognition With Lightweight Convolutional Neural Network
title_full	Contactless Multispectral Palm-Vein Recognition With Lightweight Convolutional Neural Network
title_fullStr	Contactless Multispectral Palm-Vein Recognition With Lightweight Convolutional Neural Network
title_full_unstemmed	Contactless Multispectral Palm-Vein Recognition With Lightweight Convolutional Neural Network
title_sort	contactless multispectral palm-vein recognition with lightweight convolutional neural network
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/12db00b71822455498b214fba8d9d4e6
work_keys_str_mv	AT yungyaochen contactlessmultispectralpalmveinrecognitionwithlightweightconvolutionalneuralnetwork AT chihhsienhsia contactlessmultispectralpalmveinrecognitionwithlightweightconvolutionalneuralnetwork AT pinghanchen contactlessmultispectralpalmveinrecognitionwithlightweightconvolutionalneuralnetwork
_version_	1718425207400562688

Contactless Multispectral Palm-Vein Recognition With Lightweight Convolutional Neural Network

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