Enhancing fixed size palmprint region of interest (ROI) extraction algorithm for personal identification

Identification and verification are the fundamental process in biometrics recognition system. Research indicates that palmprint, as one of the biometric recognitions system is commonly used for human identification. It is because there are many features and information contained inside the palmprint...

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Published in:International Journal of Recent Technology and Engineering
Main Author: Harun N.; Wan Abdul Rahman W.E.Z.; Aliman S.; Ramena H.; Othman N.S.
Format: Article
Language:English
Published: Blue Eyes Intelligence Engineering and Sciences Publication 2019
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85182111420&doi=10.35940%2fijrte.d5177.118419&partnerID=40&md5=5dace953381edc85c9c74436a7bf6f78
id 2-s2.0-85182111420
spelling 2-s2.0-85182111420
Harun N.; Wan Abdul Rahman W.E.Z.; Aliman S.; Ramena H.; Othman N.S.
Enhancing fixed size palmprint region of interest (ROI) extraction algorithm for personal identification
2019
International Journal of Recent Technology and Engineering
8
4
10.35940/ijrte.d5177.118419
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85182111420&doi=10.35940%2fijrte.d5177.118419&partnerID=40&md5=5dace953381edc85c9c74436a7bf6f78
Identification and verification are the fundamental process in biometrics recognition system. Research indicates that palmprint, as one of the biometric recognitions system is commonly used for human identification. It is because there are many features and information contained inside the palmprint that can be used in the identification process. However, only a small region of the palmprint can be extracted using the existing palmprint region of interest (ROI) extraction algorithms. This has become a problem for identification systems due to negligible and loss of important features which are located outside the ROI. Hence, it is a necessity to improve the palmprint ROI extraction algorithm whereby bigger palmprint ROI can be extracted using this algorithm. Therefore, a larger fixed size extraction algorithm for palmprint ROI is proposed where the extraction region is larger so that more important identification features can be captured inside these ROIs. The performance between proposed and existing extraction algorithms are tested based on two characteristics which are the palmprint ROI extraction area and the comparison of feature creases extracted in a palmprint ROI. The results show that 300x300 fixed size ROI is able to capture 13 out of 14 creases attributes for palmprint identification. This implies that the proposed extraction algorithm shows a promising method of extraction as compared to the existing algorithms. © BEIESP.
Blue Eyes Intelligence Engineering and Sciences Publication
22773878
English
Article
All Open Access; Gold Open Access
author Harun N.; Wan Abdul Rahman W.E.Z.; Aliman S.; Ramena H.; Othman N.S.
spellingShingle Harun N.; Wan Abdul Rahman W.E.Z.; Aliman S.; Ramena H.; Othman N.S.
Enhancing fixed size palmprint region of interest (ROI) extraction algorithm for personal identification
author_facet Harun N.; Wan Abdul Rahman W.E.Z.; Aliman S.; Ramena H.; Othman N.S.
author_sort Harun N.; Wan Abdul Rahman W.E.Z.; Aliman S.; Ramena H.; Othman N.S.
title Enhancing fixed size palmprint region of interest (ROI) extraction algorithm for personal identification
title_short Enhancing fixed size palmprint region of interest (ROI) extraction algorithm for personal identification
title_full Enhancing fixed size palmprint region of interest (ROI) extraction algorithm for personal identification
title_fullStr Enhancing fixed size palmprint region of interest (ROI) extraction algorithm for personal identification
title_full_unstemmed Enhancing fixed size palmprint region of interest (ROI) extraction algorithm for personal identification
title_sort Enhancing fixed size palmprint region of interest (ROI) extraction algorithm for personal identification
publishDate 2019
container_title International Journal of Recent Technology and Engineering
container_volume 8
container_issue 4
doi_str_mv 10.35940/ijrte.d5177.118419
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85182111420&doi=10.35940%2fijrte.d5177.118419&partnerID=40&md5=5dace953381edc85c9c74436a7bf6f78
description Identification and verification are the fundamental process in biometrics recognition system. Research indicates that palmprint, as one of the biometric recognitions system is commonly used for human identification. It is because there are many features and information contained inside the palmprint that can be used in the identification process. However, only a small region of the palmprint can be extracted using the existing palmprint region of interest (ROI) extraction algorithms. This has become a problem for identification systems due to negligible and loss of important features which are located outside the ROI. Hence, it is a necessity to improve the palmprint ROI extraction algorithm whereby bigger palmprint ROI can be extracted using this algorithm. Therefore, a larger fixed size extraction algorithm for palmprint ROI is proposed where the extraction region is larger so that more important identification features can be captured inside these ROIs. The performance between proposed and existing extraction algorithms are tested based on two characteristics which are the palmprint ROI extraction area and the comparison of feature creases extracted in a palmprint ROI. The results show that 300x300 fixed size ROI is able to capture 13 out of 14 creases attributes for palmprint identification. This implies that the proposed extraction algorithm shows a promising method of extraction as compared to the existing algorithms. © BEIESP.
publisher Blue Eyes Intelligence Engineering and Sciences Publication
issn 22773878
language English
format Article
accesstype All Open Access; Gold Open Access
record_format scopus
collection Scopus
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