改进ACSTL虹膜分割方法

2018 20th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC) Pub Date : 2018-09-01 DOI:10.1109/SYNASC.2018.00076

Cristina M. Noaica

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引用次数: 0

摘要

ACSTL是一种分割方法，最初是为使用lg2200 -虹膜传感器捕获的虹膜图像而开发的。ACSTL在整个LG2200数据集上提供了0.326的分割误差，但对于来自其他红外传感器的图像，如特写相机(CASIA间隔数据集)的分割误差要高得多。本文给出了一种从分割性能和平均图像执行时间两方面改进ACSTL的方法。对ACSTL的图像处理和虹膜边界选择算法进行了改进。

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Improving ACSTL Iris Segmentation Method

ACSTL is a segmentation method that was initially developed for iris images that are captured with an LG2200-iris sensor. ACSTL provided a 0.326 segmentation error on the entire LG2200 dataset, but a much higher error for images from other infrared sensors, such as a close-up camera (CASIA Interval dataset). This paper shows a method to improve ACSTL in terms of both segmentation performance and the average execution time per image. The improvements are brought to the image processing and to the iris boundary selection algorithm of ACSTL.

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来源期刊

2018 20th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)

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