快速有效的虹膜图像分割方法

针对影响虹膜识别性能的眼睑及睫毛遮挡以及瞳孔边界非标准圆等情况，提出一种新的虹膜图像分割方法．采用活动轮廓模型技术计算出瞳孔的精确边界；根据归一化虹膜图像水平方向灰度信号的小波模极值点在尺度空间的演化计算得到眼睑遮挡边界上点，并利用多项式拟合确定眼睑遮挡部分；最后结合一维Gabor滤波的眼睫毛检测结果实现有效的虹膜图像分割．与现有分割方法相比，消除了瞳孔传统圆模型给归一化虹膜图像带来的畸变影响，避免了传统眼睑边界检测过程中涉及的四维参数空间搜索范围问题,降低了算法实现复杂度．在CASIA图像库上的实验结果表明该方法可将系统识别等错误率从8%降低到4.4%．

Fast and efficient iris segmentation

A novel iris segmentation method is proposed to remove noises such as eyelashes and eyelids occlusion, and the pupil , which would possibly exist in iris regions and result in poor recognition performance. The accuracy boundary of the pupil is achieved by an active counter model technique. After computing the wavelet transform of the intensity signal of the normalized iris images along horizon direction, the position of eyelid occlusion edge points can be obtained by evaluating the behavior of the wavelet modulus maxima over scales, and the eyelid occlusion regions can be determined by polynomialfitting. Combined with the eyelashes detecting results obtained by the 1-D Gabor filter, efficient iris segmentation is achieved. Compared with the existing iris segmentation algorithms, the proposed method not only illuminates the aberration influence on the normalized iris image caused by the traditional circular model of the pupil, but debases the implement complexity by avoiding the issues of determining the searching ranges of 4-parameters spaces involved in traditional eyelid boundary detecting. Experimental results on the CASIA iris database images show that the proposed segmentation method can decrease the Equal Error Rate of the recognition system from 8% to 4.4 %.