基于SCM的虹膜干扰区域去除算法  

Iris noise removal algorithm based on SCM

作　　者：马义德[1] 周炜超[1] 滕飞[1] 绽琨[1] 赵荣昌[1] 

机构地区：[1]兰州大学信息科学与工程学院,兰州730000

出　　处：《计算机工程与应用》2013年第20期161-166,共6页Computer Engineering and Applications

基　　金：国家自然科学基金(No.60872109);中央高校基本科研业务费自由探索项目(No.LZUJBKY-2010-220)

摘　　要：为了提高虹膜识别率,提出了一种新的睫毛及眼睑区域定位算法。为了提取呈不同角度分布的睫毛,结合SCM模型和动态交叉熵准则对归一化虹膜图像进行多次迭代,根据图像的灰度分布特性,选取合适迭代结果,对多幅迭代图像进行边缘像素点跟踪融合来获得理想的干扰区域轮廓定位。采用高斯金字塔尺度变换与霍夫变换相结合的方法对眼睑的类椭圆区域进行拟合,进而获得连续的眼睑边缘,实现对归一化虹膜图像中干扰区域的准确定位。实验结果验证了方法的有效性。This paper presents a novel method of eye lashed and eyelid regional location in iris images. In order to extract the distribution of different angles eyelashes, it puts the normalized iris image to carry on many iterations using SCM. According to the image gray distribution feature, it uses cross entropy criterion and minimum crossentropy criterion to select the better itera tion results. In order to get a good noise profile targeting region, it uses tracking and fusion methods to the edge pixels in multi ple iterations images. It adopts the method of combining the Gaussian pyramid scale transform and Hough transform, fitting the elliptic region of the eyelids, to obtain a continuous edge of the eyelid, to achieve the exact location of the noise region in the normalized iris images. Experimental results confirm the validity of this method.

关 键 词：虹膜识别 睫毛检测 眼睑检测 脉冲发放皮层模型 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]