A fast iris recognition system through optimum feature extraction

, , , ,
1 Department of Computer Science and Engineering, Green University of Bangladesh, Dhaka, Bangladesh
2 Department of Computer Science and Engineering, Pabna University of Science and Technology, Pabna, Bangladesh
3 Department of Computer Science and Engineering, Varendra University, Rajshahi, Bangladesh
4 Bone Biology Division, Garvan Institute of Medical Research, NSW, Australia
5 School of Biomedical Science, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia
DOI
10.7287/peerj.preprints.27363v2
Published
Accepted
Subject Areas
Computer Vision
Keywords
Biometrics, Iris Recognition, PCA, DWT, Gabor filter, Hough Transformation, Daugman’s Rubber Sheet Model
Copyright
© 2019 Rana et al.
Licence
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
Cite this article
Rana HK, Azam MS, Akhtar MR, Quinn JMW, Moni MA. 2019. A fast iris recognition system through optimum feature extraction. PeerJ Preprints 7:e27363v2

Abstract

With an increasing demand for stringent security systems, automated identification of individuals based on biometric methods has been a major focus of research and development over the last decade. Biometric recognition analyses unique physiological traits or behavioral characteristics, such as an iris, face, retina, voice, fingerprint, hand geometry, keystrokes or gait. The iris has a complex and unique structure that remains stable over a person's lifetime, features that have led to its increasing interest in its use for biometric recognition.

In this study, we proposed a technique incorporating Principal Component Analysis (PCA) based on Discrete Wavelet Transformation (DWT) for the extraction of the optimum features of an iris and reducing the runtime needed for iris templates classification. The idea of using DWT behind PCA is to reduce the resolution of the iris template. DWT converts an iris image into four frequency sub-bands. One frequency sub-band instead of four has been used for further feature extraction by using PCA. Our experimental evaluation demonstrates the efficient performance of the proposed technique.

Author Comment

We have changed classifier in v2 of the preprint.

Supplemental Information

Code and dataset of the manuscript

DOI: 10.7287/peerj.preprints.27363v2/supp-1