Multi-level iris video image thresholding

2009 IEEE Workshop on Computational Intelligence in Biometrics: Theory, Algorithms, and Applications Pub Date : 2009-05-15 DOI:10.1109/CIB.2009.4925684

Yingzi Du, Senior Member, N. L. Thomas, Emrah Arslanturk

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引用次数: 8

Abstract

Iris recognition has been shown to be one of the most accurate biometrics. However, under non-ideal situations, its recognition accuracy can be reduced dramatically. Under such situations, video images can be used to improve the accuracy. The traditional single image based segmentation method could be inefficient. Video image based thresholding method can help improve the segmentation efficiency. However, traditional thresholding methods are not designed for iris images. In this paper, the multi-level iris video image thresholding method is proposed. It takes advantage of the correlations between consecutive images for video based thresholding. It is an orientation invariant thresholding scheme. K-mean clustering is used to find the clusters and PCA is used to quickly project the image to the clusters. The experimental results show the proposed method is effective. The thresholded images show clear pupil and iris areas, which can help further iris segmentation and processing. In addition, the proposed method can be applied to non-video images if they are obtained from the same sensor with similar illumination conditions.

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多级虹膜视频图像阈值分割

虹膜识别已被证明是最准确的生物识别技术之一。然而，在非理想情况下，其识别精度会大大降低。在这种情况下，可以使用视频图像来提高精度。传统的基于单幅图像的分割方法效率低下。基于视频图像的阈值分割方法有助于提高分割效率。然而，传统的阈值分割方法并不适用于虹膜图像。本文提出了一种多级虹膜视频图像阈值分割方法。它利用连续图像之间的相关性进行基于视频的阈值分割。它是一种方向不变的阈值格式。使用k -均值聚类来寻找聚类，并使用PCA快速将图像投影到聚类中。实验结果表明，该方法是有效的。阈值化后的图像显示出清晰的瞳孔和虹膜区域，有助于虹膜的进一步分割和处理。此外，该方法还可以应用于从相同传感器获得的具有相似照明条件的非视频图像。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

2009 IEEE Workshop on Computational Intelligence in Biometrics: Theory, Algorithms, and Applications

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