虹膜身份识别算法的研究
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摘要
在当今信息化时代,如何准确鉴别一个人的身份、保护信息安全,已成为一个必须解决的社会问题。传统的身份认证方式由于极易伪造和丢失,越来越难以满足人们的需求,目前最为便捷与安全的解决方案无疑就是生物特征识别技术。虹膜识别是各种生物特征识别技术中误识率最低的,并且具有唯一性、稳定性、可采集性、非侵犯性和防伪性等优点,有着广阔的应用前景、巨大的经济效益和科学研究价值。
虹膜识别算法主要包括虹膜图像质量评价、虹膜图像预处理、特征抽取和模式匹配等环节,其中虹膜图像预处理包括虹膜内外边界定位、眼睑定位、虹膜归一化、图像增强和噪声去除。
本文主要对虹膜识别算法进行了如下研究和创新:
1)实际采集到的虹膜图像常常存在散焦模糊和眼睑、睫毛的严重遮挡,增大了同类虹膜之间的差异,容易导致虹膜识别系统的错误拒绝率增加。本文从降低算法复杂度、减小运算量的角度出发,提出了一种快速有效的分步式虹膜图像质量评价算法:首先,使用高斯拉普拉斯算子提取瞳孔两侧虹膜局部区域的高频分量来判断图像是否散焦模糊;然后,计算瞳孔上方指定区域的平均灰度来判断虹膜区域是否受到眼睑的严重遮挡;最后,通过提取瞳孔上方指定区域的水平高频分量来衡量虹膜受睫毛遮挡的程度。该算法只需要定位瞳孔,避免了难定位、耗时长的虹膜外圆定位,并且只针对虹膜局部区域提取相关信息,不需要处理整幅图像,在CASIA 1.0和CASIA 2.0 ver1虹膜库上进行了实验,结果表明这是一种快速有效的虹膜图像质量评价算法。
2)在虹膜定位方面,针对经典算法都是在三维参数空间迭代求最优解,存在着计算量大、运行速度慢的缺点,本文提出了一种快速准确、鲁棒性好的虹膜定位方法:根据虹膜图像的灰度分布特点,巩膜、虹膜和瞳孔依次呈阶梯分布,并且瞳孔和虹膜的分界较明显,内圆定位相对容易,分别使用了最小二乘法和几何方法来定位瞳孔。然后在瞳孔定位的基础上,采用改进的Canny算子结合Hough变换的方法定位虹膜外圆。检测眼睑时,提出了采用Radon变换分段直线定位眼睑,并用阈值法去除了睫毛、眼睑阴影对虹膜区域的干扰。3)由于虹膜图像的大小和分辨率各不相同,并且还不同程度地存在着平移、缩放和旋转失真,使得虹膜模式很难直接进行比对。本文采用Daugman提出的各向同质的橡胶皮弹性模型(Rubber Sheet Model),对定位后的虹膜图像进行了归一化处理,经分析取归一化图像分辨率为512×64。为了去除眼睑、睫毛的干扰,选取归一化图像中右上角四分之一部分作为虹膜的有效区域,同时,为了避免瞳孔对有效区域的影响,舍弃前四行纹理信息,从第五行开始选取虹膜有效区域,有效部分的分辨率大小是256×32。
4)考虑到二维Gabor函数能够较好地模拟动物视觉系统中的一对简单视觉神经元的感受特性,通过选择不同的窗口、频率和方向参数,可以得到有效的纹理特征。本文设计并构造了基于极坐标形式的32通道Gabor滤波器对归一化虹膜图像提取纹理特征,并使用Hamming距离进行匹配,取得了较好的识别效果。实验中选用CASIA 1.0库中有效区域受干扰较少的53只眼睛共371幅图像作为实验用虹膜库,并取得了在阈值为0.372时,错误拒绝18次,错误接受587次,错误拒绝率为1.62 %,错误接受率为0.87 %,正确识别率为99.119 %的满意结果。
In the informatization time, how to correctly identify a person's identity and protect his information safe already becomes a social problem which needs to be solved. The traditional identity autentication methods become more and more difficult to satisfy people's requirements as a result of its easy forging and lost. At present, the most convenient and fast solution will undoubtedly be the biometrics. Because iris recognition has the least false recognition rate among all kinds of biometrics, and it has the merits of uniqueness, stability, non-invasive and antifalsification, iris recognition has the broard application prospect, huge economic benefits and research value.
Iris recognition algorithm includes iris image quality assessment, iris image pre-process, feature extraction and pattern matching, while iris image pre-process includes the localization of inner and outer circles, eyelid localization, iris normolization, image enhancement and noise removal.
In this paper, we mainly research and innovate following iris recognition parts:
1) Usually there are defocus and eyelid occlusion in the obtained iris images, which increase difference among intra-class iris images, and at last will make the false reject rate of recognition system up. To reduce the complexity of algorithm and computation, we propose a fast and efficient multiple step iris image quality assessment algorithm, which is as: Firstly the high frequency power of iris local areas lying in the two sides of the pupil is calculated by Laplacian of Gaussian operator to discard the defocused images, secondly the average gray value of the upper designated area of the pupil is computed to discard the seriously occluded images by eyelid, lastly the occluded degree by eyelash is measured through the horizontal high frequency power of the upper designated area of the pupil. Besides, the algorithm only needs to localize pupil, avoids the hard localized and time-consuming outer circle localization. In addition, the algorithm just extracts relative information from iris local areas to assess image quality, needs not to handle the whole image. We experiment it on CASIA 1.0 and CASIA 2.0 ver1 iris libraries, and the result shows that it is a fast and efficient iris image quality assessment algorithm.
2) In localizing iris, we propose a fast, accurate and robust iris localization method, while the classical methods have disadvantage of large calculated amount and consuming time because of their iteration in three-dimension parameter space. According to the gray distribution of iris image, the gray of sclera, iris and pupil is of ladder distribution, and it is obvious between pupil boundary and iris boundary, so inner circle of iris is easy to localize, and we separatsely use the least squares method and geometric method to localize pupil. Then on the basis of pupil localization, we use the improved Canny operator plus Hough transform to localize outer circle of iris. While detecting eyelids, we propose a method of Radon transform subsection line to localize eyelid, and use threshold method to remove eyelash and eyelid shadow.
3) As a result of different sizes and different resolutions of iris images and translation distortion, zoom distortion and rorate distortion with different degrees in iris images, it is hard to compare directly iris patterns. We use Daugman's Rubber Sheet Model to normalize the licalized iris, and transform the annular iris into the same size rectange which is of size 512×64. To remove the eyelid and eyelash interferences, we select the right corner quarter of normalized iris as the valid area to do the subsequent recognition, to avoid influence from pupil, we select iris pixels from the fifth row, and the valid iris area is of size 256×32.
4) Because 2D Gabor function could simulate the feeling characteristics of a pair of simple visual neurons from animals, we can use it to extract useful pattern feature through selecting different window, frequency and direction parameters. In this paper, we design and construct 32 enterclose Gabor filter based on polar form to extract pattern feature from normalized iris image, and then we use Hamming distance to match. We select 53 eyes, 371 images from CASIA 1.0, whose useful iris area is disturbed a little, and when threshold is 0.372, the false reject time is 18, the false acception time is 587, the false rejection rate is 1.62 %, the false accept rate is 0.87 %, and the correct recognition rate is 99.119 %, while the result is pleasant.
虹膜识别算法主要包括虹膜图像质量评价、虹膜图像预处理、特征抽取和模式匹配等环节,其中虹膜图像预处理包括虹膜内外边界定位、眼睑定位、虹膜归一化、图像增强和噪声去除。
本文主要对虹膜识别算法进行了如下研究和创新:
1)实际采集到的虹膜图像常常存在散焦模糊和眼睑、睫毛的严重遮挡,增大了同类虹膜之间的差异,容易导致虹膜识别系统的错误拒绝率增加。本文从降低算法复杂度、减小运算量的角度出发,提出了一种快速有效的分步式虹膜图像质量评价算法:首先,使用高斯拉普拉斯算子提取瞳孔两侧虹膜局部区域的高频分量来判断图像是否散焦模糊;然后,计算瞳孔上方指定区域的平均灰度来判断虹膜区域是否受到眼睑的严重遮挡;最后,通过提取瞳孔上方指定区域的水平高频分量来衡量虹膜受睫毛遮挡的程度。该算法只需要定位瞳孔,避免了难定位、耗时长的虹膜外圆定位,并且只针对虹膜局部区域提取相关信息,不需要处理整幅图像,在CASIA 1.0和CASIA 2.0 ver1虹膜库上进行了实验,结果表明这是一种快速有效的虹膜图像质量评价算法。
2)在虹膜定位方面,针对经典算法都是在三维参数空间迭代求最优解,存在着计算量大、运行速度慢的缺点,本文提出了一种快速准确、鲁棒性好的虹膜定位方法:根据虹膜图像的灰度分布特点,巩膜、虹膜和瞳孔依次呈阶梯分布,并且瞳孔和虹膜的分界较明显,内圆定位相对容易,分别使用了最小二乘法和几何方法来定位瞳孔。然后在瞳孔定位的基础上,采用改进的Canny算子结合Hough变换的方法定位虹膜外圆。检测眼睑时,提出了采用Radon变换分段直线定位眼睑,并用阈值法去除了睫毛、眼睑阴影对虹膜区域的干扰。3)由于虹膜图像的大小和分辨率各不相同,并且还不同程度地存在着平移、缩放和旋转失真,使得虹膜模式很难直接进行比对。本文采用Daugman提出的各向同质的橡胶皮弹性模型(Rubber Sheet Model),对定位后的虹膜图像进行了归一化处理,经分析取归一化图像分辨率为512×64。为了去除眼睑、睫毛的干扰,选取归一化图像中右上角四分之一部分作为虹膜的有效区域,同时,为了避免瞳孔对有效区域的影响,舍弃前四行纹理信息,从第五行开始选取虹膜有效区域,有效部分的分辨率大小是256×32。
4)考虑到二维Gabor函数能够较好地模拟动物视觉系统中的一对简单视觉神经元的感受特性,通过选择不同的窗口、频率和方向参数,可以得到有效的纹理特征。本文设计并构造了基于极坐标形式的32通道Gabor滤波器对归一化虹膜图像提取纹理特征,并使用Hamming距离进行匹配,取得了较好的识别效果。实验中选用CASIA 1.0库中有效区域受干扰较少的53只眼睛共371幅图像作为实验用虹膜库,并取得了在阈值为0.372时,错误拒绝18次,错误接受587次,错误拒绝率为1.62 %,错误接受率为0.87 %,正确识别率为99.119 %的满意结果。
In the informatization time, how to correctly identify a person's identity and protect his information safe already becomes a social problem which needs to be solved. The traditional identity autentication methods become more and more difficult to satisfy people's requirements as a result of its easy forging and lost. At present, the most convenient and fast solution will undoubtedly be the biometrics. Because iris recognition has the least false recognition rate among all kinds of biometrics, and it has the merits of uniqueness, stability, non-invasive and antifalsification, iris recognition has the broard application prospect, huge economic benefits and research value.
Iris recognition algorithm includes iris image quality assessment, iris image pre-process, feature extraction and pattern matching, while iris image pre-process includes the localization of inner and outer circles, eyelid localization, iris normolization, image enhancement and noise removal.
In this paper, we mainly research and innovate following iris recognition parts:
1) Usually there are defocus and eyelid occlusion in the obtained iris images, which increase difference among intra-class iris images, and at last will make the false reject rate of recognition system up. To reduce the complexity of algorithm and computation, we propose a fast and efficient multiple step iris image quality assessment algorithm, which is as: Firstly the high frequency power of iris local areas lying in the two sides of the pupil is calculated by Laplacian of Gaussian operator to discard the defocused images, secondly the average gray value of the upper designated area of the pupil is computed to discard the seriously occluded images by eyelid, lastly the occluded degree by eyelash is measured through the horizontal high frequency power of the upper designated area of the pupil. Besides, the algorithm only needs to localize pupil, avoids the hard localized and time-consuming outer circle localization. In addition, the algorithm just extracts relative information from iris local areas to assess image quality, needs not to handle the whole image. We experiment it on CASIA 1.0 and CASIA 2.0 ver1 iris libraries, and the result shows that it is a fast and efficient iris image quality assessment algorithm.
2) In localizing iris, we propose a fast, accurate and robust iris localization method, while the classical methods have disadvantage of large calculated amount and consuming time because of their iteration in three-dimension parameter space. According to the gray distribution of iris image, the gray of sclera, iris and pupil is of ladder distribution, and it is obvious between pupil boundary and iris boundary, so inner circle of iris is easy to localize, and we separatsely use the least squares method and geometric method to localize pupil. Then on the basis of pupil localization, we use the improved Canny operator plus Hough transform to localize outer circle of iris. While detecting eyelids, we propose a method of Radon transform subsection line to localize eyelid, and use threshold method to remove eyelash and eyelid shadow.
3) As a result of different sizes and different resolutions of iris images and translation distortion, zoom distortion and rorate distortion with different degrees in iris images, it is hard to compare directly iris patterns. We use Daugman's Rubber Sheet Model to normalize the licalized iris, and transform the annular iris into the same size rectange which is of size 512×64. To remove the eyelid and eyelash interferences, we select the right corner quarter of normalized iris as the valid area to do the subsequent recognition, to avoid influence from pupil, we select iris pixels from the fifth row, and the valid iris area is of size 256×32.
4) Because 2D Gabor function could simulate the feeling characteristics of a pair of simple visual neurons from animals, we can use it to extract useful pattern feature through selecting different window, frequency and direction parameters. In this paper, we design and construct 32 enterclose Gabor filter based on polar form to extract pattern feature from normalized iris image, and then we use Hamming distance to match. We select 53 eyes, 371 images from CASIA 1.0, whose useful iris area is disturbed a little, and when threshold is 0.372, the false reject time is 18, the false acception time is 587, the false rejection rate is 1.62 %, the false accept rate is 0.87 %, and the correct recognition rate is 99.119 %, while the result is pleasant.
引文
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