Iris recognition is nowadays considered as one of the most accurate biometric recognition techniques. However, the overall performances of such systems can be reduced in non-ideal conditions, such as unconstrained, on-the-move, or noncollaborative setups. In particular, a critical step of the recognition process is the segmentation of the iris pattern in the input face/eye image. This process has to deal with the fact that the iris region of the eye is a relatively small area, wet and constantly in motion due to involuntary eye movements.Moreover, eyelids, eyelashes and reflections are occlusions of the iris pattern that can cause errors in the segmentation process. As a result, an incorrect segmentation can produce erroneous biometric recognitions and seriously reduce the final accuracy of the system. This chapter reviews current state-of-the-art iris segmentation methods in different applicative scenarios. Boundary estimation methods will be discussed, along with methods designed to remove reflections and occlusions, such as eyelids and eyelashes. In the last section, the results of the main described methods applied to public image datasets are reviewed and commented.

Iris Segmentation : State of the Art and Innovative Methods / R. Donida Labati, A. Genovese, V. Piuri, F. Scotti (INTELLIGENT SYSTEMS REFERENCE LIBRARY). - In: Cross disciplinary biometric systems / [a cura di] C. Liu, V.K. Mago. - New York : Springer, 2012 Mar. - ISBN 9783642284564. - pp. 151-182 [10.1007/978-3-642-28457-1_8]

Iris Segmentation : State of the Art and Innovative Methods

R. Donida Labati;A. Genovese;V. Piuri;F. Scotti
2012-03

Abstract

Iris recognition is nowadays considered as one of the most accurate biometric recognition techniques. However, the overall performances of such systems can be reduced in non-ideal conditions, such as unconstrained, on-the-move, or noncollaborative setups. In particular, a critical step of the recognition process is the segmentation of the iris pattern in the input face/eye image. This process has to deal with the fact that the iris region of the eye is a relatively small area, wet and constantly in motion due to involuntary eye movements.Moreover, eyelids, eyelashes and reflections are occlusions of the iris pattern that can cause errors in the segmentation process. As a result, an incorrect segmentation can produce erroneous biometric recognitions and seriously reduce the final accuracy of the system. This chapter reviews current state-of-the-art iris segmentation methods in different applicative scenarios. Boundary estimation methods will be discussed, along with methods designed to remove reflections and occlusions, such as eyelids and eyelashes. In the last section, the results of the main described methods applied to public image datasets are reviewed and commented.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2434/199170
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