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Finger Vein Recognition Based on (2D)(2) PCA and Metric Learning

Finger vein recognition is a promising biometric recognition technology, which verifies identities via the vein patterns in the fingers. In this paper, (2D)(2) PCA is applied to extract features of finger veins, based on which a new recognition method is proposed in conjunction with metric learning....

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Detalles Bibliográficos
Autores principales: Yang, Gongping, Xi, Xiaoming, Yin, Yilong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3364026/
https://www.ncbi.nlm.nih.gov/pubmed/22675248
http://dx.doi.org/10.1155/2012/324249
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author Yang, Gongping
Xi, Xiaoming
Yin, Yilong
author_facet Yang, Gongping
Xi, Xiaoming
Yin, Yilong
author_sort Yang, Gongping
collection PubMed
description Finger vein recognition is a promising biometric recognition technology, which verifies identities via the vein patterns in the fingers. In this paper, (2D)(2) PCA is applied to extract features of finger veins, based on which a new recognition method is proposed in conjunction with metric learning. It learns a KNN classifier for each individual, which is different from the traditional methods where a fixed threshold is employed for all individuals. Besides, the SMOTE technology is adopted to solve the class-imbalance problem. Our experiments show that the proposed method is effective by achieving a recognition rate of 99.17%.
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spelling pubmed-33640262012-06-06 Finger Vein Recognition Based on (2D)(2) PCA and Metric Learning Yang, Gongping Xi, Xiaoming Yin, Yilong J Biomed Biotechnol Research Article Finger vein recognition is a promising biometric recognition technology, which verifies identities via the vein patterns in the fingers. In this paper, (2D)(2) PCA is applied to extract features of finger veins, based on which a new recognition method is proposed in conjunction with metric learning. It learns a KNN classifier for each individual, which is different from the traditional methods where a fixed threshold is employed for all individuals. Besides, the SMOTE technology is adopted to solve the class-imbalance problem. Our experiments show that the proposed method is effective by achieving a recognition rate of 99.17%. Hindawi Publishing Corporation 2012 2012-05-20 /pmc/articles/PMC3364026/ /pubmed/22675248 http://dx.doi.org/10.1155/2012/324249 Text en Copyright © 2012 Gongping Yang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Yang, Gongping
Xi, Xiaoming
Yin, Yilong
Finger Vein Recognition Based on (2D)(2) PCA and Metric Learning
title Finger Vein Recognition Based on (2D)(2) PCA and Metric Learning
title_full Finger Vein Recognition Based on (2D)(2) PCA and Metric Learning
title_fullStr Finger Vein Recognition Based on (2D)(2) PCA and Metric Learning
title_full_unstemmed Finger Vein Recognition Based on (2D)(2) PCA and Metric Learning
title_short Finger Vein Recognition Based on (2D)(2) PCA and Metric Learning
title_sort finger vein recognition based on (2d)(2) pca and metric learning
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3364026/
https://www.ncbi.nlm.nih.gov/pubmed/22675248
http://dx.doi.org/10.1155/2012/324249
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AT yinyilong fingerveinrecognitionbasedon2d2pcaandmetriclearning