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Demonstration Of Palm Vein Pattern Biometric Recognition By Machine Learning

This paper aims to demonstrate the extraction of palm vein pattern features by local binary pattern (LBP) and its different recognition rate by two types of classification methods. The first classification method is by K-nearest neighbour (KNN) while the second method is by a support vector machine...

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Main Authors: Mohd Noh, Zarina, Ramlee, Ridza Azri, Ahmad Radzi, Syafeeza
Format: Article
Language:English
Published: Penerbit Universiti Teknikal Malaysia Melaka 2020
Online Access:http://eprints.utem.edu.my/id/eprint/25097/2/5831-16160-1-PB.PDF
http://eprints.utem.edu.my/id/eprint/25097/
https://journal.utem.edu.my/index.php/ijhati/article/view/5831/3897
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spelling my.utem.eprints.250972021-04-16T15:46:09Z http://eprints.utem.edu.my/id/eprint/25097/ Demonstration Of Palm Vein Pattern Biometric Recognition By Machine Learning Mohd Noh, Zarina Ramlee, Ridza Azri Ahmad Radzi, Syafeeza Ramlee, Ridza Azri This paper aims to demonstrate the extraction of palm vein pattern features by local binary pattern (LBP) and its different recognition rate by two types of classification methods. The first classification method is by K-nearest neighbour (KNN) while the second method is by a support vector machine (SVM). Whilst SVM is optimized for direct classifications between two classes, the KNN is best for multi-class classifications. Based on the biometric recognition framework shared in this paper, both techniques shared comparable performance in terms of the recognition rate. The differences in the recognition rate can only be seen if the LBP features extracted for the classification are different. In general, a higher recognition rate can be achieved for palm vein pattern biometric system if all LBP bins are used for the classification, compared to if only selected features are used for the purpose. The best recognition rate that can be achieved by the three datasets demonstrated in this paper are 60%, 70% and 100% respectively for the CASIA, PolyU and self-dataset. It shows that different input dataset may behave differently even by using the same machine learning approach in its biometric recognition process. Penerbit Universiti Teknikal Malaysia Melaka 2020-04 Article PeerReviewed text en http://eprints.utem.edu.my/id/eprint/25097/2/5831-16160-1-PB.PDF Mohd Noh, Zarina and Ramlee, Ridza Azri and Ahmad Radzi, Syafeeza and Ramlee, Ridza Azri (2020) Demonstration Of Palm Vein Pattern Biometric Recognition By Machine Learning. International Journal of Human and Technology Interaction, 4 (1). pp. 11-18. ISSN 2590-3551 https://journal.utem.edu.my/index.php/ijhati/article/view/5831/3897
institution Universiti Teknikal Malaysia Melaka
building UTEM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknikal Malaysia Melaka
content_source UTEM Institutional Repository
url_provider http://eprints.utem.edu.my/
language English
description This paper aims to demonstrate the extraction of palm vein pattern features by local binary pattern (LBP) and its different recognition rate by two types of classification methods. The first classification method is by K-nearest neighbour (KNN) while the second method is by a support vector machine (SVM). Whilst SVM is optimized for direct classifications between two classes, the KNN is best for multi-class classifications. Based on the biometric recognition framework shared in this paper, both techniques shared comparable performance in terms of the recognition rate. The differences in the recognition rate can only be seen if the LBP features extracted for the classification are different. In general, a higher recognition rate can be achieved for palm vein pattern biometric system if all LBP bins are used for the classification, compared to if only selected features are used for the purpose. The best recognition rate that can be achieved by the three datasets demonstrated in this paper are 60%, 70% and 100% respectively for the CASIA, PolyU and self-dataset. It shows that different input dataset may behave differently even by using the same machine learning approach in its biometric recognition process.
format Article
author Mohd Noh, Zarina
Ramlee, Ridza Azri
Ahmad Radzi, Syafeeza
Ramlee, Ridza Azri
spellingShingle Mohd Noh, Zarina
Ramlee, Ridza Azri
Ahmad Radzi, Syafeeza
Ramlee, Ridza Azri
Demonstration Of Palm Vein Pattern Biometric Recognition By Machine Learning
author_facet Mohd Noh, Zarina
Ramlee, Ridza Azri
Ahmad Radzi, Syafeeza
Ramlee, Ridza Azri
author_sort Mohd Noh, Zarina
title Demonstration Of Palm Vein Pattern Biometric Recognition By Machine Learning
title_short Demonstration Of Palm Vein Pattern Biometric Recognition By Machine Learning
title_full Demonstration Of Palm Vein Pattern Biometric Recognition By Machine Learning
title_fullStr Demonstration Of Palm Vein Pattern Biometric Recognition By Machine Learning
title_full_unstemmed Demonstration Of Palm Vein Pattern Biometric Recognition By Machine Learning
title_sort demonstration of palm vein pattern biometric recognition by machine learning
publisher Penerbit Universiti Teknikal Malaysia Melaka
publishDate 2020
url http://eprints.utem.edu.my/id/eprint/25097/2/5831-16160-1-PB.PDF
http://eprints.utem.edu.my/id/eprint/25097/
https://journal.utem.edu.my/index.php/ijhati/article/view/5831/3897
_version_ 1698700626940133376
score 13.211869

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