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Iris Localization Algorithm based on Effective Area

机译:基于有效区域的虹膜定位算法

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Iris localization is the most crucial part of the iris processing because its accuracy can directly affect the accuracy of biometric identification in subsequent steps. Yet, the quality of iris images may be sharply degraded due to interference from eyelashes and reflections during image acquisition, which can affect the localization accuracy adversely. To solve the problem, an iris localization algorithm based on effective area is proposed. First, YOLOv4 is used to crop the image to obtain the effective iris area, which is beneficial in improving the accuracy of subsequent localization. Furthermore, a method to remove reflective noise is proposed, which can effectively avoid the problem of noise interference in the process of inner boundary determination. Finally, aiming at the edge deviation caused by eyelashes, an outer boundary adjustment method is proposed. The experimental results show that the proposed method achieves good performance in the localization of iris images of both good quality and noise interference and outperforms other state-of-the-art methods.
机译:虹膜本地化是虹膜加工的最重要部分,因为其精度可以直接影响后续步骤中的生物识别的准确性。然而,虹膜图像的质量可能会劣化急剧由于图像采集,这可能不利地影响定位精确度期间从睫毛和反射的干扰。为了解决问题,提出了一种基于有效区域的虹膜定位算法。首先,YOLOV4用于裁剪图像以获得有效的虹膜区域,这有利于提高后续定位的准确性。此外,提出了一种去除反射噪声的方法,其可以有效地避免内部边界确定过程中的噪声干扰问题。最后,针对引起睫毛边缘偏差,外部边界调整方法提出。实验结果表明,该方法在虹灯图像的定位方面取得了良好的性能,既良好的质量和噪声干扰,也优于其他最先进的方法。

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