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Conference papers

Improved Iris Recognition Using Parabolic Normalization and Multi-Layer Perceptron Neural Network

Alaa Hilal 1 Bassam Daya 2 Pierre Beauseroy 3
1 STMR - Sciences et Technologies pour la Maitrise des Risques
UTT - Université de Technologie de Troyes, CNRS - Centre National de la Recherche Scientifique : UMR6279
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.
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Conference papers
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https://hal-utt.archives-ouvertes.fr/hal-02518593
Contributor : Jean-Baptiste Vu Van Connect in order to contact the contributor
Submitted on : Wednesday, March 25, 2020 - 12:39:44 PM
Last modification on : Friday, August 27, 2021 - 3:14:06 PM

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  • HAL Id : hal-02518593, version 1

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UTT | CNRS

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Alaa Hilal, Bassam Daya, Pierre Beauseroy. Improved Iris Recognition Using Parabolic Normalization and Multi-Layer Perceptron Neural Network. 4th NCTA, Oct 2012, Barcelona, Spain. ⟨hal-02518593⟩

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