Toward Accurate and Fast Iris Segmentation for Iris Biometrics
Toward Accurate and Fast Iris Segmentation for Iris Biometrics
复制标题
实现虹膜生物识别的准确快速虹膜分割
DOI：
10.1109/tpami.2008.183
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发表时间：
2009-09-01
影响因子：
23.6
通讯作者：
Qiu, Xianchao
中科院分区：
文献类型：
--
作者：
He, Zhaofeng;Tan, Tieniu;Qiu, Xianchao
虹膜分割是虹膜识别中必不可少的模块，因为它定义了用于后续处理(如特征提取)的有效图像区域。传统的虹膜分割方法往往涉及对大参数空间的穷举搜索，耗时长，对噪声敏感。针对这些问题，本文提出了一种新的准确快速的虹膜分割算法。在有效地去除反射后，首先构建一个Adaboost级联虹膜检测器来提取虹膜中心的粗略位置。然后检测虹膜边界的边缘点，建立一种称为拉力和推力的弹性模型。在该模型下，圆形虹膜边界的中心和半径以胡克定律的恢复力驱动的方式迭代细化。在此基础上，针对非圆形虹膜边界问题，提出了一种基于光滑样条线的边缘拟合方法。之后，通过边缘检测和曲线拟合来定位眼皮。这里的新奇之处在于采用了等级过滤器来消除噪声，直方图过滤器来处理眼皮形状的不规则性。最后，通过学习的预测模型检测睫毛和阴影。该模型通过分析不同虹膜区域的灰度分布，为睫毛和阴影检测提供了一个自适应的阈值。在三个具有挑战性的虹膜图像库上的实验结果表明，该算法在准确率和速度上都优于现有的方法。
Iris segmentation is an essential module in iris recognition because it defines the effective image region used for subsequent processing such as feature extraction. Traditional iris segmentation methods often involve an exhaustive search of a large parameter space, which is time consuming and sensitive to noise. To address these problems, this paper presents a novel algorithm for accurate and fast iris segmentation. After efficient reflection removal, an Adaboost-cascade iris detector is first built to extract a rough position of the iris center. Edge points of iris boundaries are then detected, and an elastic model named pulling and pushing is established. Under this model, the center and radius of the circular iris boundaries are iteratively refined in a way driven by the restoring forces of Hooke's law. Furthermore, a smoothing spline-based edge fitting scheme is presented to deal with noncircular iris boundaries. After that, eyelids are localized via edge detection followed by curve fitting. The novelty here is the adoption of a rank filter for noise elimination and a histogram filter for tackling the shape irregularity of eyelids. Finally, eyelashes and shadows are detected via a learned prediction model. This model provides an adaptive threshold for eyelash and shadow detection by analyzing the intensity distributions of different iris regions. Experimental results on three challenging iris image databases demonstrate that the proposed algorithm outperforms state-of-the-art methods in both accuracy and speed.