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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....
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2012
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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%. |
format | Online Article Text |
id | pubmed-3364026 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT yanggongping fingerveinrecognitionbasedon2d2pcaandmetriclearning AT xixiaoming fingerveinrecognitionbasedon2d2pcaandmetriclearning AT yinyilong fingerveinrecognitionbasedon2d2pcaandmetriclearning |