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Fast, accurate and memory efficient pupil localization based on pixels properties method

机译:基于像素属性方法的快速,准确和高效存储的瞳孔定位

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One of the most crucial processes in the iris recognition system is iris segmentation. In iris segmentation, the iris region is extracted from the eyelids, eyelashes, eyebrows, pupil and sclera to identify the uniqueness of its patterns. The segmentation process is crucial since the poor and false segmentation can affect the accuracy of the iris recognition system. Since the system will deal with a lot of irises, it must be fast enough to execute the iris segmentation. The speed also crucial in order to implement it in real-time. Pupil localization is a part of iris segmentation process which is to locate pupil boundaries. The pupil boundaries can provide the information of pupil center and radius as a based to locate the iris boundaries. This research is important since poor pupil localization due to reflections and eyelashes interferences can reduce the segmentation accuracy of the iris recognition system. Moreover, the modified segmentation methods based on Hough transform and Integro-differential operator are still complex and require a lot of time. Furthermore, there are many pupil and iris localization methods that assume the shape of pupil as a circle which is not accurate. In this paper, the new pupil localization method which is based on pixels properties is proposed to obtain fast, accurate and memory efficient of pupil localization in the iris recognition system. The proposed method is compared with other methods based on localization accuracy, time to execute and memory usage. According to the results, the proposed method recorded high localization accuracy, low execution time and low memory usage than the other state-of-the-art methods. This proves that the proposed pupil localization method is appropriate and suitable to be implemented in real-time systems.
机译:虹膜识别系统中最关键的过程之一是虹膜分割。在虹膜分割中,从眼睑,睫毛,眉毛,瞳孔和巩膜中提取虹膜区域,以识别其图案的独特性。分割过程至关重要,因为不良和错误的分割会影响虹膜识别系统的准确性。由于系统将处理很多虹膜,因此它必须足够快才能执行虹膜分割。速度对于实时实施也至关重要。学生定位是虹膜分割过程的一部分,该过程旨在定位瞳孔边界。瞳孔边界可以基于瞳孔中心和半径的信息来定位虹膜边界。这项研究非常重要,因为由于反射和睫毛干扰而导致的瞳孔定位不良会降低虹膜识别系统的分割精度。而且,基于霍夫变换和整数微分算子的改进的分割方法仍然很复杂并且需要很多时间。此外,有许多瞳孔和虹膜定位方法将瞳孔的形状假定为不准确的圆形。本文提出了一种新的基于像素特性的瞳孔定位方法,以在虹膜识别系统中获得快速,准确和高效的瞳孔定位记忆。基于定位精度,执行时间和内存使用情况,将该方法与其他方法进行了比较。根据结果​​,与其他现有技术相比,该方法具有较高的定位精度,较低的执行时间和较低的内存使用率。这证明了所提出的瞳孔定位方法是合适的并且适合于在实时系统中实现。

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