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Comparitive Analysis of Conventional, Real and Complex Wavelet Transforms for Iris Recognition

Published on July 2016 by Mansa S. Mane, Sunil M. Sangve
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International Conference on Internet of Things, Next Generation Networks and Cloud Computing
Foundation of Computer Science USA
ICINC2016 - Number 2
July 2016
Authors: Mansa S. Mane, Sunil M. Sangve
49b58c69-4f12-4e51-86ad-c0d6e23254eb

Mansa S. Mane, Sunil M. Sangve . Comparitive Analysis of Conventional, Real and Complex Wavelet Transforms for Iris Recognition. International Conference on Internet of Things, Next Generation Networks and Cloud Computing. ICINC2016, 2 (July 2016), 14-17.

@article{
author = { Mansa S. Mane, Sunil M. Sangve },
title = { Comparitive Analysis of Conventional, Real and Complex Wavelet Transforms for Iris Recognition },
journal = { International Conference on Internet of Things, Next Generation Networks and Cloud Computing },
issue_date = { July 2016 },
volume = { ICINC2016 },
number = { 2 },
month = { July },
year = { 2016 },
issn = 0975-8887,
pages = { 14-17 },
numpages = 4,
url = { /proceedings/icinc2016/number2/25530-4801/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference on Internet of Things, Next Generation Networks and Cloud Computing
%A Mansa S. Mane
%A Sunil M. Sangve
%T Comparitive Analysis of Conventional, Real and Complex Wavelet Transforms for Iris Recognition
%J International Conference on Internet of Things, Next Generation Networks and Cloud Computing
%@ 0975-8887
%V ICINC2016
%N 2
%P 14-17
%D 2016
%I International Journal of Computer Applications
Abstract

Iris Recognition System is a process of recognizingan individual by analyzing the random pattern of iris and comparing with database. In this paper comparative analysis is perform with wavelet transform such as 2D Discrete wavelet transform (2D-DWT), Real dual tree Discrete wavelet transform (R-DT-DWT) and Complex dual tree Discrete wavelet transform (C-DT-DWT) for iris recognition. These approaches are tested on various databases. The process starts from pre-processing. In pre-processing stage the image is enhanced, segmented and normalized. Now smoothed image is taken into consideration for feature extraction using above mentioned wavelet transforms. Finally image is applied post-classifier for reducing false rejection rate.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Feature Extraction Post-classifier Pre=processing And Wavelet Transform.