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A robust eyelash detection based on iris focus assessment

机译:基于虹膜聚焦评估的强大睫毛检测

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摘要

For accurate iris recognition, it is essential to detect eyelash regions and remove them for iris code generation, since eyelashes act as noise factors in the iris recognition. In addition, eyelash positions can be changed for enrollment and recognition and this may cause FR (false rejection). To overcome these problems, we propose a new method for detecting eyelashes in this paper. This work shows three advantages over previous works. First, because eyelash detection was performed based on focus assessment, its performance was not affected by image blurring. Second, the new focus assessment method is appropriate for iris images. Third, the detected eyelash regions were not used for iris code generation and therefore iris recognition accuracy was greatly enhanced. Experimental results showed that the eyelash detection error was about 0.96% when using the CASIA DB and iris recognition accuracy with eyelash detection was enhanced more than 0.86% of EER when compared to the EER obtained without eyelash detection.
机译:对于精确的虹膜识别,至关重要的是检测睫毛区域并将其去除以生成虹膜代码,因为睫毛在虹膜识别中起着噪声因子的作用。此外,可以更改睫毛位置进行注册和识别,这可能会导致FR(假剔除)。为了克服这些问题,我们提出了一种检测睫毛的新方法。这项工作比以前的工作显示出三个优点。首先,由于睫毛检测是基于焦点评估进行的,因此其性能不受图像模糊的影响。其次,新的焦点评估方法适用于虹膜图像。第三,检测到的睫毛区域没有用于虹膜代码生成,因此虹膜识别精度大大提高。实验结果表明,使用CASIA DB时,睫毛检测误差约为0.96%,与不使用睫毛检测获得的EER相比,使用睫毛检测的虹膜识别准确度提高了EER的0.86%以上。

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