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Performance Evaluation of Multimodal Multifeature Authentication System Using KNN Classification
This research proposes a multimodal multifeature biometric system for human recognition using two traits, that is, palmprint and iris. The purpose of this research is to analyse integration of multimodal and multifeature biometric system using feature level fusion to achieve better performance. The...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4657114/ https://www.ncbi.nlm.nih.gov/pubmed/26640813 http://dx.doi.org/10.1155/2015/762341 |
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author | Rajagopal, Gayathri Palaniswamy, Ramamoorthy |
author_facet | Rajagopal, Gayathri Palaniswamy, Ramamoorthy |
author_sort | Rajagopal, Gayathri |
collection | PubMed |
description | This research proposes a multimodal multifeature biometric system for human recognition using two traits, that is, palmprint and iris. The purpose of this research is to analyse integration of multimodal and multifeature biometric system using feature level fusion to achieve better performance. The main aim of the proposed system is to increase the recognition accuracy using feature level fusion. The features at the feature level fusion are raw biometric data which contains rich information when compared to decision and matching score level fusion. Hence information fused at the feature level is expected to obtain improved recognition accuracy. However, information fused at feature level has the problem of curse in dimensionality; here PCA (principal component analysis) is used to diminish the dimensionality of the feature sets as they are high dimensional. The proposed multimodal results were compared with other multimodal and monomodal approaches. Out of these comparisons, the multimodal multifeature palmprint iris fusion offers significant improvements in the accuracy of the suggested multimodal biometric system. The proposed algorithm is tested using created virtual multimodal database using UPOL iris database and PolyU palmprint database. |
format | Online Article Text |
id | pubmed-4657114 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
spelling | pubmed-46571142015-12-06 Performance Evaluation of Multimodal Multifeature Authentication System Using KNN Classification Rajagopal, Gayathri Palaniswamy, Ramamoorthy ScientificWorldJournal Research Article This research proposes a multimodal multifeature biometric system for human recognition using two traits, that is, palmprint and iris. The purpose of this research is to analyse integration of multimodal and multifeature biometric system using feature level fusion to achieve better performance. The main aim of the proposed system is to increase the recognition accuracy using feature level fusion. The features at the feature level fusion are raw biometric data which contains rich information when compared to decision and matching score level fusion. Hence information fused at the feature level is expected to obtain improved recognition accuracy. However, information fused at feature level has the problem of curse in dimensionality; here PCA (principal component analysis) is used to diminish the dimensionality of the feature sets as they are high dimensional. The proposed multimodal results were compared with other multimodal and monomodal approaches. Out of these comparisons, the multimodal multifeature palmprint iris fusion offers significant improvements in the accuracy of the suggested multimodal biometric system. The proposed algorithm is tested using created virtual multimodal database using UPOL iris database and PolyU palmprint database. Hindawi Publishing Corporation 2015 2015-11-10 /pmc/articles/PMC4657114/ /pubmed/26640813 http://dx.doi.org/10.1155/2015/762341 Text en Copyright © 2015 G. Rajagopal and R. Palaniswamy. https://creativecommons.org/licenses/by/3.0/ 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 Rajagopal, Gayathri Palaniswamy, Ramamoorthy Performance Evaluation of Multimodal Multifeature Authentication System Using KNN Classification |
title | Performance Evaluation of Multimodal Multifeature Authentication System Using KNN Classification |
title_full | Performance Evaluation of Multimodal Multifeature Authentication System Using KNN Classification |
title_fullStr | Performance Evaluation of Multimodal Multifeature Authentication System Using KNN Classification |
title_full_unstemmed | Performance Evaluation of Multimodal Multifeature Authentication System Using KNN Classification |
title_short | Performance Evaluation of Multimodal Multifeature Authentication System Using KNN Classification |
title_sort | performance evaluation of multimodal multifeature authentication system using knn classification |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4657114/ https://www.ncbi.nlm.nih.gov/pubmed/26640813 http://dx.doi.org/10.1155/2015/762341 |
work_keys_str_mv | AT rajagopalgayathri performanceevaluationofmultimodalmultifeatureauthenticationsystemusingknnclassification AT palaniswamyramamoorthy performanceevaluationofmultimodalmultifeatureauthenticationsystemusingknnclassification |