The detection of the iris boundaries is considered in the literature as one of the most critical steps in the identification task of the iris recognition systems. In this paper we present an iterative approach to the detection of the iris center and boundaries by using neural networks. The proposed algorithm starts by an initial random point in the input image, then it processes a set of local image properties in a circular region of interest searching for the peculiar transition patterns of the iris boundaries. A trained neural network processes the parameters associated to the extracted boundaries and it estimates the offsets in the vertical and horizontal axis with respect to the estimated center. The coordinates of the starting point are then updated with the processed offsets. The steps are then iterated for a fixed number of epochs, producing an iterative refinements of the coordinates of the pupils center and its boundaries. Experiments showed that the method is feasible and it can be exploited even in non-ideal operative condition of iris recognition biometric systems.

Neural-based iterative approach for iris detection in iris recognition systems / R. Donida Labati, V. Piuri, F. Scotti - In: Proceedings of the 2009 IEEE Symposium on Computational Intelligence in Security and Defense Applications (CISDA 2009)Ottawa : IEEE, 2009 Dec 18. - ISBN 978-1-4244-3763-4. - pp. 1-6 (( convegno IEEE Symposium on Computational Intelligence for Security and Defense Applications, 2009. CISDA 2009 tenutosi a Ottawa nel 2009 [10.1109/CISDA.2009.5356533].

Neural-based iterative approach for iris detection in iris recognition systems

R. Donida Labati;V. Piuri;F. Scotti
2009-12-18

Abstract

The detection of the iris boundaries is considered in the literature as one of the most critical steps in the identification task of the iris recognition systems. In this paper we present an iterative approach to the detection of the iris center and boundaries by using neural networks. The proposed algorithm starts by an initial random point in the input image, then it processes a set of local image properties in a circular region of interest searching for the peculiar transition patterns of the iris boundaries. A trained neural network processes the parameters associated to the extracted boundaries and it estimates the offsets in the vertical and horizontal axis with respect to the estimated center. The coordinates of the starting point are then updated with the processed offsets. The steps are then iterated for a fixed number of epochs, producing an iterative refinements of the coordinates of the pupils center and its boundaries. Experiments showed that the method is feasible and it can be exploited even in non-ideal operative condition of iris recognition biometric systems.
Settore INF/01 - Informatica
IEEE
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2434/147902
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