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Recognition of unideal iris images using region-based active contour model and game theory

机译:基于区域主动轮廓模型和博弈论的虹膜非理想图像识别

摘要

We process the unideal iris images that are acquired in an unconstrained situation and are affected severely by gaze deviations, eyelid and eyelash occlusions, non uniform intensities, motion blurs, reflections, etc. The proposed unideal iris recognition algorithm has two novelties as compared to the previous works; firstly, we propose to deploy a region-based active contour model to segment an unideal iris image with intensity inhomogeneity; Secondly, an iterative algorithm, called the Modified Contribution- Selection Algorithm (MCSA), is used in the context of coalitional game theory to select a subset of informative features without compromising the recognition rate. The verification performance of the proposed scheme is validated using the UBIRIS Version 1, the ICE 2005, and the WVU Unideal datasets.
机译:我们处理在不受约束的情况下获取的并受到注视偏差,眼睑和睫毛遮挡,不均匀强度,运动模糊,反射等严重影响的非理想虹膜图像。与相比,拟议的非理想虹膜识别算法具有两个新颖之处以前的作品;首先,我们建议采用基于区域的主动轮廓模型来分割强度不均匀的不理想虹膜图像。其次,在联盟博弈理论的背景下,使用一种称为改进贡献选择算法(MCSA)的迭代算法,在不影响识别率的情况下选择信息特征的子集。使用UBIRIS版本1,ICE 2005和WVU Unideal数据集对提出的方案的验证性能进行了验证。

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