A Robust and Fast Computation Touchless Palm Print Recognition System Using LHEAT and the IFkNCN Classifier
Mobile implementation is a current trend in biometric design.This paper proposes a new approach to palm print recognition, in which smart phones are used to capture palm print images at a distance.Atouchless systemwas developed because of public demand for privacy and sanitation. Robust hand track...
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| Format: | Article |
| Language: | en |
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Hindawi Publishing Corporation
2015
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| Online Access: | http://eprints.usm.my/38193/1/A_Robust_and_Fast_Computation_Touchless_Palm_Print_Recognition_System_Using.pdf http://eprints.usm.my/38193/ http://dx.doi.org/10.1155/2015/360217 |
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| _version_ | 1834501432102879232 |
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| author | Jaafar, Haryati Ibrahim, Salwani Ramli, Dzati Athiar |
| author_facet | Jaafar, Haryati Ibrahim, Salwani Ramli, Dzati Athiar |
| author_sort | Jaafar, Haryati |
| building | Hamzah Sendut Library |
| collection | Institutional Repository |
| content_provider | Universiti Sains Malaysia |
| content_source | USM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Mobile implementation is a current trend in biometric design.This paper proposes a new approach to palm print recognition, in
which smart phones are used to capture palm print images at a distance.Atouchless systemwas developed because of public demand
for privacy and sanitation. Robust hand tracking, image enhancement, and fast computation processing algorithms are required for
effective touchless and mobile-based recognition. In this project, hand tracking and the region of interest (ROI) extractionmethod
were discussed. A sliding neighborhood operation with local histogram equalization, followed by a local adaptive thresholding
or LHEAT approach, was proposed in the image enhancement stage to manage low-quality palm print images. To accelerate the
recognition process, a new classifier, improved fuzzy-based k nearest centroid neighbor (IFkNCN), was implemented. By removing
outliers and reducing the amount of training data, this classifier exhibited faster computation. Our experimental results demonstrate
that a touchless palm print system using LHEAT and IFkNCN achieves a promising recognition rate of 98.64%. |
| format | Article |
| id | my.usm.eprints.38193 |
| institution | Universiti Sains Malaysia |
| language | en |
| publishDate | 2015 |
| publisher | Hindawi Publishing Corporation |
| record_format | eprints |
| spelling | my.usm.eprints.38193 http://eprints.usm.my/38193/ A Robust and Fast Computation Touchless Palm Print Recognition System Using LHEAT and the IFkNCN Classifier Jaafar, Haryati Ibrahim, Salwani Ramli, Dzati Athiar TK1-9971 Electrical engineering. Electronics. Nuclear engineering Mobile implementation is a current trend in biometric design.This paper proposes a new approach to palm print recognition, in which smart phones are used to capture palm print images at a distance.Atouchless systemwas developed because of public demand for privacy and sanitation. Robust hand tracking, image enhancement, and fast computation processing algorithms are required for effective touchless and mobile-based recognition. In this project, hand tracking and the region of interest (ROI) extractionmethod were discussed. A sliding neighborhood operation with local histogram equalization, followed by a local adaptive thresholding or LHEAT approach, was proposed in the image enhancement stage to manage low-quality palm print images. To accelerate the recognition process, a new classifier, improved fuzzy-based k nearest centroid neighbor (IFkNCN), was implemented. By removing outliers and reducing the amount of training data, this classifier exhibited faster computation. Our experimental results demonstrate that a touchless palm print system using LHEAT and IFkNCN achieves a promising recognition rate of 98.64%. Hindawi Publishing Corporation 2015 Article PeerReviewed application/pdf en http://eprints.usm.my/38193/1/A_Robust_and_Fast_Computation_Touchless_Palm_Print_Recognition_System_Using.pdf Jaafar, Haryati and Ibrahim, Salwani and Ramli, Dzati Athiar (2015) A Robust and Fast Computation Touchless Palm Print Recognition System Using LHEAT and the IFkNCN Classifier. Computational Intelligence and Neuroscience, 2015 (360217). pp. 1-17. ISSN 1687-5265 http://dx.doi.org/10.1155/2015/360217 |
| spellingShingle | TK1-9971 Electrical engineering. Electronics. Nuclear engineering Jaafar, Haryati Ibrahim, Salwani Ramli, Dzati Athiar A Robust and Fast Computation Touchless Palm Print Recognition System Using LHEAT and the IFkNCN Classifier |
| title | A Robust and Fast Computation Touchless Palm Print
Recognition System Using LHEAT and the IFkNCN Classifier |
| title_full | A Robust and Fast Computation Touchless Palm Print
Recognition System Using LHEAT and the IFkNCN Classifier |
| title_fullStr | A Robust and Fast Computation Touchless Palm Print
Recognition System Using LHEAT and the IFkNCN Classifier |
| title_full_unstemmed | A Robust and Fast Computation Touchless Palm Print
Recognition System Using LHEAT and the IFkNCN Classifier |
| title_short | A Robust and Fast Computation Touchless Palm Print
Recognition System Using LHEAT and the IFkNCN Classifier |
| title_sort | robust and fast computation touchless palm print
recognition system using lheat and the ifkncn classifier |
| topic | TK1-9971 Electrical engineering. Electronics. Nuclear engineering |
| url | http://eprints.usm.my/38193/1/A_Robust_and_Fast_Computation_Touchless_Palm_Print_Recognition_System_Using.pdf http://eprints.usm.my/38193/ http://dx.doi.org/10.1155/2015/360217 |
| url_provider | http://eprints.usm.my/ |