Sensitivity Analysis of a Class of Iris Localization Algorithms to Blurring Effect
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This paper presents a study of a class of iris localization algorithms in the presence of blurring. The effect of blurring is a serious problem in most image processing systems. It may originate in iris imaging systems due to out-of-focus effect. It affects the features extracted from the iris images. Hence, the objective of this paper is to study the sensitivity of three popular iris localization algorithms to the presence of blurring. Features are extracted from normal as well as blurred iris images and used for iris localization. Moreover, Wiener filter restoration is used as a tool to combat the effect of blurring. Performance of the compared iris localization algorithms with Wiener filter restoration is also studied. Simulation results reveal that Masek iris localization algorithm has the least sensitivity to the blurring effect. Its accuracy without blurring is 88.2%, and with blurring, it decreases to 68.18%. Moreover, the Wiener filter significantly improves the accuracy of iris localization.
KeywordsIris localization Masek code Integro-differential operator Blurring
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