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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi Publishing Corporation
2015
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| Subjects: | |
| Online Access: | http://eprints.usm.my/38193/ http://eprints.usm.my/38193/1/A_Robust_and_Fast_Computation_Touchless_Palm_Print_Recognition_System_Using.pdf |
| Summary: | 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%. |
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