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Insulin Chart Prediction for Diabetic Patients Using Hidden Markov Model (HMM) and Simulated Annealing Method Read chapter Read first chapter

2014 | OriginalPaper | Chapter

Palmprint Recognition Using Geometrical and Statistical Constraints

Authors : Aditya Nigam, Phalguni Gupta

Published in: Proceedings of the Second International Conference on Soft Computing for Problem Solving (SocProS 2012), December 28-30, 2012

Publisher: Springer India

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Abstract

This paper proposes an efficient biometrics system based on palmprint. Palmprint ROI is transformed using proposed local edge pattern (LEP). Corner like features are extracted from the enhanced palmprint images as they are stable and highly discriminative. It has also proposed a distance measure that uses some geometrical and statistical constraints to track corner feature points between two palmprint ROI’s. The performance of the proposed system is tested on publicly available PolyU database consisting of 7,752 and CASIA database consisting of 5,239 hand images. The feature extraction as well as matching capabilities of the proposed system are optimized and it is found to perform with CRR of \(99.97\,\%\) with ERR of \(0.66\,\%\) for PolyU and CRR of \(100\,\%\) with ERR of \(0.24\,\%\) on CASIA databases respectively.

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Metadata
Title
Palmprint Recognition Using Geometrical and Statistical Constraints
Authors
Aditya Nigam
Phalguni Gupta
Copyright Year
2014
Publisher
Springer India
Book
Proceedings of the Second International Conference on Soft Computing for Problem Solving (SocProS 2012), December 28-30, 2012

Print ISBN: 978-81-322-1601-8

Electronic ISBN: 978-81-322-1602-5

Copyright Year: 2014

DOI
https://doi.org/10.1007/978-81-322-1602-5_136

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