基于形态学算子的睫毛干扰抑制算法

针对虹膜识别中的睫毛干扰抑制问题,本文提出了一种基于形态学算子的睫毛干扰抑制算法.该算法将虹膜图像中的睫毛对象看作是背景图像中存在的随机的,不规则的细微裂缝,通过膨胀算子对这些裂缝进行像素填补,实现了对睫毛干扰的有效抑制.与常见的高斯滤波法相比,所提算法具有更强的睫毛干扰抑制能力.通过CASIA虹膜库的仿真实验表明,所提算法比高斯滤波法消除的睫毛像素点要多40%,可以使Daugman和Wildes定位算法的定位精度分别提高1.7%和2%,定位时间分别减少27.9%和24.2%.

Suppressing Eyelash Interference Based on Morphological Operator

In order to improve the performance of eyelash interference suppressing algorithm in iris recognition method,a new algorithm based on Morphological operators was developed.Considering the eyelash as the free nondirectional silt in the background of eye image,the suppression of eyelash interference can be taken as a process to fill up these fine slits by using dilation operator.Compared with Gaussian filter algorithm,the proposed algorithm can suppress more eyelash interference,and has better performance.The experiment results from the CASIA iris database show that the proposed method can reduce more 40% eyelash pixels than that of the Gaussian filter algorithm.Using the proposed method,the accuracy of the Daugman's algorithm and Wildes' algorithm,which are traditional iris location algorithm,are improved 1.7% and 2% respectively,and the location time are decreased by 27.9% and 24.2% respectively.