Improved Iris Recognition using Parabolic Normalization and Multi-layer Perceptron Neural Network

A. Hilal, B. Daya, P. Beauseroy

Abstract

Iris signature is considered as one of the richest, unique, and stable biometrics. This permits to an iris identification system to identify a person even after many years from his first iris signature extraction. In this paper we investigate a new method of iris normalization where iris features are normalized in a parabolic function. Thus iris information close to the pupil is privileged to that close to the sclera. A multilayer perceptron artificial neural network is then used to test the normalization effect and compare it with classical linear normalization method. The study is tested on CASIA V3 database iris images. Accuracy at the equal error rate operating point and receiver operating characteristics curves show better results with the parabolic normalization method and thus propose its use for better iris recognition system performance.

References

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Paper Citation


in Harvard Style

Hilal A., Daya B. and Beauseroy P. (2012). Improved Iris Recognition using Parabolic Normalization and Multi-layer Perceptron Neural Network . In Proceedings of the 4th International Joint Conference on Computational Intelligence - Volume 1: NCTA, (IJCCI 2012) ISBN 978-989-8565-33-4, pages 643-646. DOI: 10.5220/0004155406430646


in Bibtex Style

@conference{ncta12,
author={A. Hilal and B. Daya and P. Beauseroy},
title={Improved Iris Recognition using Parabolic Normalization and Multi-layer Perceptron Neural Network},
booktitle={Proceedings of the 4th International Joint Conference on Computational Intelligence - Volume 1: NCTA, (IJCCI 2012)},
year={2012},
pages={643-646},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004155406430646},
isbn={978-989-8565-33-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 4th International Joint Conference on Computational Intelligence - Volume 1: NCTA, (IJCCI 2012)
TI - Improved Iris Recognition using Parabolic Normalization and Multi-layer Perceptron Neural Network
SN - 978-989-8565-33-4
AU - Hilal A.
AU - Daya B.
AU - Beauseroy P.
PY - 2012
SP - 643
EP - 646
DO - 10.5220/0004155406430646