虹膜生物特征的提取与识别
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摘要
在维护国家安全、信息安全、金融安全、航空安全等众多领域,都需要对人的真实身份进行有效鉴别,而生物特征识别技术是解决此问题的最为有效的途径。虹膜具有丰富和独特的纹理特征,非常适用于身份鉴别。基于虹膜的生物特征识别技术由于其可靠性好、识别率高和天然的防伪性等优点,有着广阔的应用前景,因而得到学术界和企业界越来越广泛的关注,成为生物特征识别中的一个非常活跃的研究领域。本论文从虹膜自身的生理结构特性出发,对虹膜识别中的一些关键技术进行了较为深入的研究,提出了一些新的思想和算法。本文的主要工作和贡献如下:
1)研究了虹膜图像的质量评估。考虑到低质量虹膜图像的引入会影响系统的识别性能。因此,有必要对获取图像进行质量评估,只有满足一定质量要求的图像才能用于识别。在现有方法的基础上,分析影响虹膜图像质量的因素,结合虹膜图像的具体特点,提出一种客观的虹膜图像质量评价方法。该方法根据虹膜图像总体清晰度和纹理清晰度来评价图像清晰度,采用可见度的粗评估和细评估计算虹膜的有效区域,从而既可以快速地识别出质量极差的图像又可以对其他图像进行定量评估。
2)研究了虹膜图像的预处理。针对现有虹膜预处理中一些算法运算速度慢、鲁棒性差和精度不高的问题,本文在前人研究的基础上,对虹膜识别算法的预处理进行了改进。预处理过程包括虹膜定位、归一化和虹膜图像的去噪及增强,重点研究了虹膜定位算法,主要是虹膜内外边缘定位、眼睑和上睫毛检测。在现有模型和算法的基础上,提出由粗到精的虹膜定位思想,此方法缩小了搜索范围,提高了速度。对虹膜内边缘,采用两步定位,先采用一种边缘检测结合最小二乘法对瞳孔粗定位,然后采用基于无初始化水平集的主动轮廓法获得瞳孔的真实边界;在瞳孔粗定位的基础上,利用改进的Hough变换的方法定位外边缘;采用最小二乘法拟合抛物线来确定上下眼睑的位置;运用双阈值法能快速地检测出绝大多数上睫毛。预处理算法在四种不同的虹膜库上进行测试,并从定位时间和准确率来评价算法的性能。
3)研究了虹膜的特征提取及匹配。提取稳定而又具有良好区别特性的虹膜特征是虹膜识别的一个关键问题。为此,本文提出三种不同的虹膜特征提取与识别的方法,所提算法和传统方法相比具有潜在的优越性。
首先,鉴于曲波(Curvelet)变换能从不同的尺度和方向分解图像,能充分地描述虹膜图像的直线或曲线纹理特征,本文提出了基于Curvelet变换的特征提取方法。根据曲波分解的低频子带和高频子带所表征的图像信息,分别采用不同的特征提取方式。在认证模式和识别模式下验证了算法的性能。相对常用的基于变换的特征提取方法来说,用虹膜的曲波分解系数来提取特征能更好地反映虹膜的主要特征。
其次,由于轮廓波(Contourlet)变换继承了Curvelet变换的各向异性的多尺度关系,能够提取图像中重要的内在几何结构特征,本文提出一种基于Contourlet变换的纹理特征提取方法。该方法采用三种不同的结构形式提取虹膜特征,将不同尺度、不同方向子带系数矩阵的均值、方差、不变矩和能量作为纹理特征,分别采用支持向量机(SVM)分类器和基于距离的匹配方法进行识别。实验表明该算法可以有效地提取虹膜纹理特征信息。
最后,考虑到经验模态分解(EMD)具有多尺度特性,能自适应地对信号进行分解,可以有效地提取虹膜的全局和局部信息,能克服小波变换提取特征时需人为选取小波基的困难,本文提出一种基于EMD和奇异值分解(SVD)的虹膜特征提取方法。采用SVD对内涵本质函数(IMF)进行分解,所得到的特征既能有效地表征虹膜又能降低特征维数,同时其编码长度和时间相对Garbor小波和Harr小波有较明显的改进,最后用Modest AdaBoost分类器进行分类。该方法在CASIA Ver1.0库中可获得高达98.9%的识别率。结果表明,利用EMD提取出的虹膜特征具有很好的区分能力,该方法获得的识别效果令人满意。
An effective identification for true identity of person is need in the field of national security, information security, financial security, aviation safety and so on. However, biometric recog-nition technology is the most effective way to solve this problem. Iris has rich and unique texture feature, which is an idea approach for personal identification. Due to biometric recog-nition based iris has better reliability, high recognition rate, natural security, and has wide ap-plication prospect. Meantime, it has been received more and more attention by the academic circles and the business community, which becomes a very active research area in the biomet-ric recognition. In this thesis, the key technologies of iris recognition are studied from the physical structural characteristics of iris itself. At the same time, some new idea and algo-rithms of iris recognition are proposed. The main work and contributions of this thesis are as follows:
1) Iris image quality assessment is studied in the thesis. Considering the input of poor iris images will affect the recognition performance, image quality need to be assessed. Only im-ages that meet certain quality can be used to identify. On the basis of the existing methods, an objective iris image quality assessment algorithm is proposed by analyzing the factors affect-ing the quality of iris image and the specific characteristics of iris image. The method evalu-ates the clarity of image from overall clarity and texture one, and calculates the effective area of iris from the visibility of crude assessment and fine assessment. It not only can quickly recognize the badly quality image but also evaluates an image from quantitatively.
2) The preprocessing of iris image is studied in this thesis. The existing iris preprocess-ing algorithms are of low executing speed, poor robustness and accuracy, an improved pre-processing approach is proposed at the basis of the predecessors’studies. Iris image process-ing includes iris location, normalization, denoising and enhancement. Especial, iris location is an emphasis in the preprocessing, it includes inner and outer boundary of iris location, eyelid and upper eyelash detection. A location method of coarse-to-fine is presented on the basis of the existing models and algorithms, then, the search domain is reduced and the location speed is improved. Edge detection combing with least squares to locate rough pupil, using active contour based without re-initialization level set method to locate its fine boundary. The outer boundary is located with the improved Hough transform method on the basis of rough pupil location. The upper and lower eyelids are detected by fitting a parabola with least-squares. Most upper eyelashes can be quickly detected with double threshold method. The preprocess algorithm tests on four kinds of different iris databases and evaluate the algorithm perform-ance with the location time and accuracy rate.
3) Iris feature extraction and matching methods are studied in the thesis. Extracting stable and distinguish iris features is the key of iris recognition. Three different iris feature extrac-tion and recognition methods are proposed in this thesis. The proposed algorithms have po-tential advantage, it compared with the traditional iris recognition methods, Firstly, in view of curvelet transform decomposes image from different scales and direc-tions, which adequately describe the image texture feature of a straight line or curve. So, an iris feature extraction method based curvelet transformation is put forward. Different feature extraction ways are adapted according to information that the low frequency and high fre-quency sub-bands presented. The algorithm performance is verified in the verification mode and identification mode. Compared with traditional transform-based extraction methods, iris features extract from curvelet coefficients can be better presented the mainly iris features. Secondly, due to contourlet inherited the anisotropic multi-scale relations of curvelet trans-form, which can extract the important intrinsic geometry features of image. Iris texture feature extraction method based on contourlet transform is proposed. The method uses three different structure to extract iris texture features that the mean, variance, moment invariants and energy from different scales and directions of sub-band coefficients. Support vector machine classi-fier and distance-based matching method are adopted to recognition. The experimental results demonstrate that the iris texture feature information can be effectively extracted by the pro-posed method.
Finally, considering of empirical mode decomposition (EMD) has a multi-scale character-istic, which can adaptively decompose the signal and effectively extract the global and part information. At the same time, EMD can overcome the difficult of selecting wavelet basis function when wavelet transform is used to extract feature. So, an iris feature extraction method based on EMD and singular value decomposition (SVD) is presented in this thesis. The decomposition features using SVD to decompose element of intrinsic mode functions, which can effectively describe iris feature and reduce the feature dimensions. Meanwhile, compared to the feature extraction of wavelet based Garbor and Harr, code time and length are clearly improveed. At last, Modest AdaBoost classifier is adapted to four kinds of iris im-age database. The recognition rate reaches 98.9% when it tested on the iris image database of CASIA Ver1.0. Results show that iris features extracting by EMD have better ability to dis-tinguish other iris.The results show that feature extracted by EMD have good discrimination, which can obtain satisfied identification result.
1)研究了虹膜图像的质量评估。考虑到低质量虹膜图像的引入会影响系统的识别性能。因此,有必要对获取图像进行质量评估,只有满足一定质量要求的图像才能用于识别。在现有方法的基础上,分析影响虹膜图像质量的因素,结合虹膜图像的具体特点,提出一种客观的虹膜图像质量评价方法。该方法根据虹膜图像总体清晰度和纹理清晰度来评价图像清晰度,采用可见度的粗评估和细评估计算虹膜的有效区域,从而既可以快速地识别出质量极差的图像又可以对其他图像进行定量评估。
2)研究了虹膜图像的预处理。针对现有虹膜预处理中一些算法运算速度慢、鲁棒性差和精度不高的问题,本文在前人研究的基础上,对虹膜识别算法的预处理进行了改进。预处理过程包括虹膜定位、归一化和虹膜图像的去噪及增强,重点研究了虹膜定位算法,主要是虹膜内外边缘定位、眼睑和上睫毛检测。在现有模型和算法的基础上,提出由粗到精的虹膜定位思想,此方法缩小了搜索范围,提高了速度。对虹膜内边缘,采用两步定位,先采用一种边缘检测结合最小二乘法对瞳孔粗定位,然后采用基于无初始化水平集的主动轮廓法获得瞳孔的真实边界;在瞳孔粗定位的基础上,利用改进的Hough变换的方法定位外边缘;采用最小二乘法拟合抛物线来确定上下眼睑的位置;运用双阈值法能快速地检测出绝大多数上睫毛。预处理算法在四种不同的虹膜库上进行测试,并从定位时间和准确率来评价算法的性能。
3)研究了虹膜的特征提取及匹配。提取稳定而又具有良好区别特性的虹膜特征是虹膜识别的一个关键问题。为此,本文提出三种不同的虹膜特征提取与识别的方法,所提算法和传统方法相比具有潜在的优越性。
首先,鉴于曲波(Curvelet)变换能从不同的尺度和方向分解图像,能充分地描述虹膜图像的直线或曲线纹理特征,本文提出了基于Curvelet变换的特征提取方法。根据曲波分解的低频子带和高频子带所表征的图像信息,分别采用不同的特征提取方式。在认证模式和识别模式下验证了算法的性能。相对常用的基于变换的特征提取方法来说,用虹膜的曲波分解系数来提取特征能更好地反映虹膜的主要特征。
其次,由于轮廓波(Contourlet)变换继承了Curvelet变换的各向异性的多尺度关系,能够提取图像中重要的内在几何结构特征,本文提出一种基于Contourlet变换的纹理特征提取方法。该方法采用三种不同的结构形式提取虹膜特征,将不同尺度、不同方向子带系数矩阵的均值、方差、不变矩和能量作为纹理特征,分别采用支持向量机(SVM)分类器和基于距离的匹配方法进行识别。实验表明该算法可以有效地提取虹膜纹理特征信息。
最后,考虑到经验模态分解(EMD)具有多尺度特性,能自适应地对信号进行分解,可以有效地提取虹膜的全局和局部信息,能克服小波变换提取特征时需人为选取小波基的困难,本文提出一种基于EMD和奇异值分解(SVD)的虹膜特征提取方法。采用SVD对内涵本质函数(IMF)进行分解,所得到的特征既能有效地表征虹膜又能降低特征维数,同时其编码长度和时间相对Garbor小波和Harr小波有较明显的改进,最后用Modest AdaBoost分类器进行分类。该方法在CASIA Ver1.0库中可获得高达98.9%的识别率。结果表明,利用EMD提取出的虹膜特征具有很好的区分能力,该方法获得的识别效果令人满意。
An effective identification for true identity of person is need in the field of national security, information security, financial security, aviation safety and so on. However, biometric recog-nition technology is the most effective way to solve this problem. Iris has rich and unique texture feature, which is an idea approach for personal identification. Due to biometric recog-nition based iris has better reliability, high recognition rate, natural security, and has wide ap-plication prospect. Meantime, it has been received more and more attention by the academic circles and the business community, which becomes a very active research area in the biomet-ric recognition. In this thesis, the key technologies of iris recognition are studied from the physical structural characteristics of iris itself. At the same time, some new idea and algo-rithms of iris recognition are proposed. The main work and contributions of this thesis are as follows:
1) Iris image quality assessment is studied in the thesis. Considering the input of poor iris images will affect the recognition performance, image quality need to be assessed. Only im-ages that meet certain quality can be used to identify. On the basis of the existing methods, an objective iris image quality assessment algorithm is proposed by analyzing the factors affect-ing the quality of iris image and the specific characteristics of iris image. The method evalu-ates the clarity of image from overall clarity and texture one, and calculates the effective area of iris from the visibility of crude assessment and fine assessment. It not only can quickly recognize the badly quality image but also evaluates an image from quantitatively.
2) The preprocessing of iris image is studied in this thesis. The existing iris preprocess-ing algorithms are of low executing speed, poor robustness and accuracy, an improved pre-processing approach is proposed at the basis of the predecessors’studies. Iris image process-ing includes iris location, normalization, denoising and enhancement. Especial, iris location is an emphasis in the preprocessing, it includes inner and outer boundary of iris location, eyelid and upper eyelash detection. A location method of coarse-to-fine is presented on the basis of the existing models and algorithms, then, the search domain is reduced and the location speed is improved. Edge detection combing with least squares to locate rough pupil, using active contour based without re-initialization level set method to locate its fine boundary. The outer boundary is located with the improved Hough transform method on the basis of rough pupil location. The upper and lower eyelids are detected by fitting a parabola with least-squares. Most upper eyelashes can be quickly detected with double threshold method. The preprocess algorithm tests on four kinds of different iris databases and evaluate the algorithm perform-ance with the location time and accuracy rate.
3) Iris feature extraction and matching methods are studied in the thesis. Extracting stable and distinguish iris features is the key of iris recognition. Three different iris feature extrac-tion and recognition methods are proposed in this thesis. The proposed algorithms have po-tential advantage, it compared with the traditional iris recognition methods, Firstly, in view of curvelet transform decomposes image from different scales and direc-tions, which adequately describe the image texture feature of a straight line or curve. So, an iris feature extraction method based curvelet transformation is put forward. Different feature extraction ways are adapted according to information that the low frequency and high fre-quency sub-bands presented. The algorithm performance is verified in the verification mode and identification mode. Compared with traditional transform-based extraction methods, iris features extract from curvelet coefficients can be better presented the mainly iris features. Secondly, due to contourlet inherited the anisotropic multi-scale relations of curvelet trans-form, which can extract the important intrinsic geometry features of image. Iris texture feature extraction method based on contourlet transform is proposed. The method uses three different structure to extract iris texture features that the mean, variance, moment invariants and energy from different scales and directions of sub-band coefficients. Support vector machine classi-fier and distance-based matching method are adopted to recognition. The experimental results demonstrate that the iris texture feature information can be effectively extracted by the pro-posed method.
Finally, considering of empirical mode decomposition (EMD) has a multi-scale character-istic, which can adaptively decompose the signal and effectively extract the global and part information. At the same time, EMD can overcome the difficult of selecting wavelet basis function when wavelet transform is used to extract feature. So, an iris feature extraction method based on EMD and singular value decomposition (SVD) is presented in this thesis. The decomposition features using SVD to decompose element of intrinsic mode functions, which can effectively describe iris feature and reduce the feature dimensions. Meanwhile, compared to the feature extraction of wavelet based Garbor and Harr, code time and length are clearly improveed. At last, Modest AdaBoost classifier is adapted to four kinds of iris im-age database. The recognition rate reaches 98.9% when it tested on the iris image database of CASIA Ver1.0. Results show that iris features extracting by EMD have better ability to dis-tinguish other iris.The results show that feature extracted by EMD have good discrimination, which can obtain satisfied identification result.
引文
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