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Authors: A. Hilal 1 ; B. Daya 2 and P. Beauseroy 3

Affiliations: 1 Université de Technologie de Troyes and Lebanese University, France ; 2 Lebanese University, Lebanon ; 3 Université de Technologie de Troyes, France

Keyword(s): Iris Recognition, Iris Normalization, Multi Layer Perceptron Neural Networks, Classification.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Enterprise Information Systems ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Image Processing and Artificial Vision Applications ; Methodologies and Methods ; Neural Network Software and Applications ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Theory and Methods

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.

CC BY-NC-ND 4.0

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Paper citation in several formats:
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 (IJCCI 2012) - NCTA; ISBN 978-989-8565-33-4; ISSN 2184-3236, SciTePress, pages 643-646. DOI: 10.5220/0004155406430646

@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 (IJCCI 2012) - NCTA},
year={2012},
pages={643-646},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004155406430646},
isbn={978-989-8565-33-4},
issn={2184-3236},
}

TY - CONF

JO - Proceedings of the 4th International Joint Conference on Computational Intelligence (IJCCI 2012) - NCTA
TI - Improved Iris Recognition using Parabolic Normalization and Multi-layer Perceptron Neural Network
SN - 978-989-8565-33-4
IS - 2184-3236
AU - Hilal, A.
AU - Daya, B.
AU - Beauseroy, P.
PY - 2012
SP - 643
EP - 646
DO - 10.5220/0004155406430646
PB - SciTePress