Palm-Vein Classification Based on Principal Orientation Features

Palm-Vein Classification Based on Principal Orientation Features

Personal recognition using palm–vein patterns has emerged as a promising alternative for human recognition because of its uniqueness, stability, live body identification, flexibility, and difficulty to cheat. With the expanding application of palm–vein pattern recognition, the corresponding growth o...

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Main Authors: Zhou, Yujia, Liu, Yaqin, Feng, Qianjin, Yang, Feng, Huang, Jing, Nie, Yixiao
Format: Online
Language:English
Published: Public Library of Science 2014
Online Access:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4226569/
id pubmed-4226569
recordtype oai_dc
spelling pubmed-42265692014-11-13 Palm-Vein Classification Based on Principal Orientation Features Zhou, Yujia Liu, Yaqin Feng, Qianjin Yang, Feng Huang, Jing Nie, Yixiao Research Article Personal recognition using palm–vein patterns has emerged as a promising alternative for human recognition because of its uniqueness, stability, live body identification, flexibility, and difficulty to cheat. With the expanding application of palm–vein pattern recognition, the corresponding growth of the database has resulted in a long response time. To shorten the response time of identification, this paper proposes a simple and useful classification for palm–vein identification based on principal direction features. In the registration process, the Gaussian-Radon transform is adopted to extract the orientation matrix and then compute the principal direction of a palm–vein image based on the orientation matrix. The database can be classified into six bins based on the value of the principal direction. In the identification process, the principal direction of the test sample is first extracted to ascertain the corresponding bin. One-by-one matching with the training samples is then performed in the bin. To improve recognition efficiency while maintaining better recognition accuracy, two neighborhood bins of the corresponding bin are continuously searched to identify the input palm–vein image. Evaluation experiments are conducted on three different databases, namely, PolyU, CASIA, and the database of this study. Experimental results show that the searching range of one test sample in PolyU, CASIA and our database by the proposed method for palm–vein identification can be reduced to 14.29%, 14.50%, and 14.28%, with retrieval accuracy of 96.67%, 96.00%, and 97.71%, respectively. With 10,000 training samples in the database, the execution time of the identification process by the traditional method is 18.56 s, while that by the proposed approach is 3.16 s. The experimental results confirm that the proposed approach is more efficient than the traditional method, especially for a large database. Public Library of Science 2014-11-10 /pmc/articles/PMC4226569/ /pubmed/25383715 http://dx.doi.org/10.1371/journal.pone.0112429 Text en © 2014 Zhou et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
repository_type Open Access Journal
institution_category Foreign Institution
institution US National Center for Biotechnology Information
building NCBI PubMed
collection Online Access
language English
format Online
author Zhou, Yujia
Liu, Yaqin
Feng, Qianjin
Yang, Feng
Huang, Jing
Nie, Yixiao
spellingShingle Zhou, Yujia
Liu, Yaqin
Feng, Qianjin
Yang, Feng
Huang, Jing
Nie, Yixiao
Palm-Vein Classification Based on Principal Orientation Features
author_facet Zhou, Yujia
Liu, Yaqin
Feng, Qianjin
Yang, Feng
Huang, Jing
Nie, Yixiao
author_sort Zhou, Yujia
title Palm-Vein Classification Based on Principal Orientation Features
title_short Palm-Vein Classification Based on Principal Orientation Features
title_full Palm-Vein Classification Based on Principal Orientation Features
title_fullStr Palm-Vein Classification Based on Principal Orientation Features
title_full_unstemmed Palm-Vein Classification Based on Principal Orientation Features
title_sort palm-vein classification based on principal orientation features
description Personal recognition using palm–vein patterns has emerged as a promising alternative for human recognition because of its uniqueness, stability, live body identification, flexibility, and difficulty to cheat. With the expanding application of palm–vein pattern recognition, the corresponding growth of the database has resulted in a long response time. To shorten the response time of identification, this paper proposes a simple and useful classification for palm–vein identification based on principal direction features. In the registration process, the Gaussian-Radon transform is adopted to extract the orientation matrix and then compute the principal direction of a palm–vein image based on the orientation matrix. The database can be classified into six bins based on the value of the principal direction. In the identification process, the principal direction of the test sample is first extracted to ascertain the corresponding bin. One-by-one matching with the training samples is then performed in the bin. To improve recognition efficiency while maintaining better recognition accuracy, two neighborhood bins of the corresponding bin are continuously searched to identify the input palm–vein image. Evaluation experiments are conducted on three different databases, namely, PolyU, CASIA, and the database of this study. Experimental results show that the searching range of one test sample in PolyU, CASIA and our database by the proposed method for palm–vein identification can be reduced to 14.29%, 14.50%, and 14.28%, with retrieval accuracy of 96.67%, 96.00%, and 97.71%, respectively. With 10,000 training samples in the database, the execution time of the identification process by the traditional method is 18.56 s, while that by the proposed approach is 3.16 s. The experimental results confirm that the proposed approach is more efficient than the traditional method, especially for a large database.
publisher Public Library of Science
publishDate 2014
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4226569/
_version_ 1613154508907479040

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