基于多分辨率分析的虹膜识别算法研究
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摘要
随着当今信息化社会的发展,个人身份鉴别的重要性正日益显现,上至国家安全、下至百姓生活,无不对其提出越来越高的要求。对于我国这样一个人口大国,身份鉴别有着尤其广泛的应用前景和重要的战略意义。生物特征认证,作为上个世纪末期才开始蓬勃发展的身份鉴别技术,利用人本身所拥有的生理特征和行为特征来判别个人身份,必将逐步取代传统的身份鉴别方法,在社会生活中占据越来越重要的地位。虹膜识别技术是近年来逐步兴起的生物特征识别技术,以其高精确度、非接触式采集、易于使用等优点日益引起学术界和企业界的关注,被广泛认为是未来最具有发展前途的生物特征识别技术。
多分辨率分析是现代图像处理一种很重要的工具,能够对图像进行多尺度的分解和表示,以寻找单一分辨率下无法发现的特性,更有效的提取图像特征,获取图像信息。本文以基于人体虹膜的生物特征识别技术研究为题,在深入研究多分辨率分析理论的最新进展和虹膜识别的主要特点的基础上,重点围绕多尺度下虹膜图像特征的有效提取以及与其相适应的虹膜图像预处理和特征匹配算法,展开一系列有益的探索。本文首先简要综述了生物特征识别的基本原理以及目前常用的生物特征识别技术,重点对虹膜识别技术的主要特点、发展历史、关键组成部分及其研究现状进行了详细的分析和总结；接下来,在对现有传统虹膜预处理方法进行深入研究的基础上,提出一种更有利于二维虹膜图像纹理信息特征提取的虹膜图像归一化方法；然后基于对多尺度几何分析理论的研究,采用一种经典的多尺度几何分析方法——Contourlet变换提取虹膜图像的多尺度多方向性信息作为虹膜特征矢量,应用支持向量机对虹膜进行分类和识别；进而本文针对Contourlet变换中实现尺度分解的拉普拉斯金字塔存在的一些不足,在对目前常用多分辨率分析算法进行比较分析后,采用一种性能更加优越的圆周对称滤波器应用于虹膜图像的多分辨率分解,以获取更加稳定而准确的虹膜特征,获得更好的识别性能。本文还将近年来逐渐受到研究者关注的自适应非平稳信号分析方法——经验模态分解应用于虹膜识别,通过实验分析捕捉到最适合虹膜分类的频带进行虹膜特征编码,取得了理想的识别性能。
本文的主要创新之处在于：
1、提出一种非极坐标展开的虹膜图像归一化方案
传统的虹膜图像归一化方案都是在极坐标下,采用映射的方式将类似环形的虹膜图像沿角度方向展开为固定尺寸的矩形,以实现虹膜图像的尺度不变性和平移不变性。这种方法简易有效,被以往所有的虹膜识别系统所采用。然而。由于虹膜内外圆半径具有较大差异,这一方案在展开的过程中势必进行大量的插值或抽取,改变了原始虹膜图像纹理几何结构和方向信息,而且由于要避开眼睑与睫毛遮挡区域,实际使用的虹膜有效区域是一个狭窄的长方形图像,这对于以往采用局部信息或将二维虹膜图像转化为一维信号进行特征提取的算法影响可能不大,当采用虹膜全局信息或纹理方向信息作为特征的算法,难免会产生不利的影响。为此本文提出一种不需要将其极坐标展开为长条矩形的归一化方法,保留了虹膜图像纹理的原始几何结构和方向信息,利用周边原始虹膜图像反转映射的策略填充内圆中空区域,以免引入新的边界。得到的图像在尺寸上更加均衡,并且可以根据虹膜图像采集的质量选取有效区域,灵活的避开眼睑与睫毛的遮挡,为多尺度几何分析或其他特征提取算法提供了更多的选择。
2、提出一种基于Contourlet变换和支持向量机的虹膜识别系统
多尺度几何分析方法和理论是近年来逐渐兴起的图像处理方法,其在对二维图像的稀疏表示上所展示出的优越性能倍受人们称赞。本文采用一种经典的多尺度几何分析算法——Contourlet变换作为工具提取虹膜图像全局纹理信息,以经过Contourlet变换分解后的多尺度多方向子带的归一化能量作为特征矢量,应用支持向量机进行分类识别,通过实验,本文遍历性的尝试了不同的尺度分解级数和各尺度下不同的分解方向数,列出各种方案下的识别率,为多尺度几何分析在虹膜识别中的进一步应用研究提供了有益的探索和指导。
3、提出一种改进的圆周对称滤波器组应用于虹膜特征提取的方法
多分辨率分析理论经过二十多年的发展,已成为图像处理不可或缺的重要工具,实现图像多分辨率分析的简单有效的结构就是一系列分辨率逐步降低的金字塔形图像集合,对图像不同分辨率下的分析非常有利于模式识别。本文在简要分析比较目前常用金字塔分解算法的基础上,采用一种改进的圆周对称滤波器组用于虹膜图像的多分辨率分解,不仅可以从理论上消除频谱混叠现象,而且具有平移和旋转不变性,使虹膜图像各尺度上的分量更加准确和稳定；并结合方向滤波器组对虹膜图像进行各尺度下的方向分解,获取虹膜多尺度多方向性信息进行识别,实验结果表明,这种方法可以获得更好的识别效果。
4、提出一种基于经验模态分解的虹膜识别算法
经验模态分解是一种新颖的信号分析方法,它可以自适应的将非平稳、非线性信号分解成一组稳态、线性的固有模态函数分量,这些固有模态函数分量相互独立,突出了信号的局部特征,对应着不同的频带信息,从而实现信号的多分辨率分解。本文利用这一特性,将虹膜图像分解为一系列按频率由高到低排列的固有模态函数分量,以互信息测度为初步评判依据,并进一步通过大量实验分析筛选出有利于虹膜识别的固有模态函数分量及其组合,对其进行有效的特征编码,并使用运算量较小的汉明距离匹配算法进行分类识别。这一方法不仅可以简化虹膜图像预处理过程,而且可以达到很高的识别性能。
本文的工作是为了丰富现有虹膜识别算法而进行的一些有益的探索和尝试,实验结果表明,所提出的虹膜识别算法基本满足实际应用的要求,在此基础上开展进一步的研究并加以完善,可以开发出我国具有自主知识产权的虹膜识别系统。
With the development of information technology, the importance of personal authentication is becoming increasingly apparent. Higher and higher requirements are imposed on it from national security to everyone's daily life. For our country with so large a population, identity authentication has broad application prospects and strategic importance. Biometric identification, as an authentication technology with flourishing development at the end of the last century, makes use of a person's physiological and behavior characteristics to determine personal identity. It plays an increasingly important role in our social life and will gradually take the place of the traditional identification methods. Iris recognition is a biometric recognition technology emerging in recent years. It has attracted the attentions of academic and business community for its advantages such as high accuracy, non-contact collection, easy to use etc. It is widely considered as the most promising biometric recognition technology in the future.
Multi-resolution analysis is a very important tool in modern digital image processing. It can represent an image in a multi-scale manner and can be used to find the image characteristics and extract image features unavailable in the pixel domain. With Iris recognition technology as the topic and based on the deeply study on the multi-resolution theory and the main features of iris recognition, this thesis researches on the efficient feature extraction algorithms of Iris images under multi-resolution framework and corresponding preprocessing and feature matching algorithms. Firstly, the principle of biometric recognition and the commonly used biometric recognition methods are introduced briefly, with emphasis on the main features, history, the key components and the current research status of iris recognition technology. Then, after investigating the existing preprocessing methods in depth, a normalization method for the Iris image is proposed which is more suitable for extracting the texture features from the 2D Iris image. Subsequently, based on the Multi-scale Geometric Analysis (MGA) theory, the classic contourlet transform is adopted to extract the multi-scale and multi-directional information as the feature vector, and support vector machine is used to classify these features for recognition. Next, for the deficiency of the Laplacian pyramid in contourlet transform, after comparing the commonly used multi-resolution analysis methods, a superior circular symmetric filter is applied to decompose the Iris image in order to get more stable and accurate Iris features and thus better the recognition performance. In addition, an adaptive non-stationary signal analysis method-empirical mode decomposition is used for Iris recognition, the frequency bands most suitable for recognition are identified through experiments and ideal recognition performance is achieved.
The main innovations of this thesis include:
1. A normalization scheme of the iris image is proposed with non-polar expansion
In traditional methods, the Iris image is normalized under polar coordinate, the annular-like Iris image is mapped into a rectangular region of fixed size along the angular direction, in this way, the scale and translation invariance can be achieved. This method is simple and effective, so it is widely used in all previous Iris recognition systems. However, because of the difference between the radii of inner and outer circles, a large number of interpolation and decimation operations must be taken during the expansion process, the texture structure and directional information of the Iris image are inevitably altered. And what's more, in order to avoid the regions with eyelids and eyelashes occlusion, the available region is just a narrow rectangular image, this may take little effect on the feature extraction methods of using local information or transforming the Iris image into a 1D signal. But surely, it will make a negative impact on the algorithms with global information or directional information as the features. To solve this problem, a normalization method is proposed in which there is no need to expand the polar coordinate into a rectangular. This method can preserve the original geometric structure and directional information of the Iris texture, and a reverse mapping strategy is used to fill in the region in the inner circle avoiding introducing new boundaries. The obtained normalized image is more balanced in size, and the effective region for Iris recognition can be selected flexibly according to the acquisition quality, which provides more choices for the feature extraction methods using multi-scale geometric analysis and other methods.
2. An Iris recognition system based on contourlet transform and SVM is proposed
Multi-scale Geometric Analysis methods have superior performance in sparsely representing the 2D image. In this thesis, contourlet transform is used to extract the global texture information of an Iris image, the normalized energies of the multi-scale and multi-directional subbands act as the feature vector, and the SVM is adopted for recognition. Experiments have been designed to try every possible combination of the decomposition level and number of directions used in an exhaustive manner, and the recognition rates under all schemes are listed, which can provide an instruction for further research on the Iris recognition systems based on MGA.
3. An Iris feature extraction method based on an improved circular symmetric multi-resolution decomposition scheme is proposed
The Multi-resolution analysis theory has become the indispensable tool in image processing community. The analysis process under different resolutions has advantageous for pattern recognition. Based on the comparisons of the classic pyramid decomposition algorithms, an improved circular symmetric filter bank is used for Iris image decomposition. It avoids the frequency aliasing effect and owns translation and rotation invariance, which makes the Iris information in different scales more accurate and stable. Then the directional filter bank is adopted to get the directional information for recognition. Experimental results show that the proposed scheme can achieve better recognition rates.
4. An Iris recognition approach based on empirical mode decomposition is proposed
The empirical mode decomposition(EMD) can adaptively decompose a non-stationary, nonlinear signal into a series of stationary and linear intrinsic mode functions(IMF). These IMFs are independent of each other and reflect the local characteristics of a signal. EMD can be regarded as a multi-resolution decomposition method with each IMF corresponding to a different frequency band. In the proposed method, the Iris image is decomposed by EMD into a few IMFs from high to low frequencies, then the IMFs or their combinations most suitable for recognition are determined with mutual information criterion as a primitive evaluation and large amount of experimental analysis. The selected IMF or IMFs are coded effectively and Hamming distance is adopted as the matching algorithm for recognition. Experiment results show that this method can not only simplify the preprocessing procedure, but can achieve surprisingly high recognition performance.
The work in this thesis can be regarded as some useful exploration and attempt in order to enrich the existing Iris recognition algorithms. Experimental results show that the proposed iris recognition algorithms can basically meet the requirements of practical applications. Based on this work, further deep researches can be conducted to improve the algorithms' performance, and an iris recognition system with independent intellectual property of China is expected to be developed.further research can be carried out based on it.
多分辨率分析是现代图像处理一种很重要的工具,能够对图像进行多尺度的分解和表示,以寻找单一分辨率下无法发现的特性,更有效的提取图像特征,获取图像信息。本文以基于人体虹膜的生物特征识别技术研究为题,在深入研究多分辨率分析理论的最新进展和虹膜识别的主要特点的基础上,重点围绕多尺度下虹膜图像特征的有效提取以及与其相适应的虹膜图像预处理和特征匹配算法,展开一系列有益的探索。本文首先简要综述了生物特征识别的基本原理以及目前常用的生物特征识别技术,重点对虹膜识别技术的主要特点、发展历史、关键组成部分及其研究现状进行了详细的分析和总结；接下来,在对现有传统虹膜预处理方法进行深入研究的基础上,提出一种更有利于二维虹膜图像纹理信息特征提取的虹膜图像归一化方法；然后基于对多尺度几何分析理论的研究,采用一种经典的多尺度几何分析方法——Contourlet变换提取虹膜图像的多尺度多方向性信息作为虹膜特征矢量,应用支持向量机对虹膜进行分类和识别；进而本文针对Contourlet变换中实现尺度分解的拉普拉斯金字塔存在的一些不足,在对目前常用多分辨率分析算法进行比较分析后,采用一种性能更加优越的圆周对称滤波器应用于虹膜图像的多分辨率分解,以获取更加稳定而准确的虹膜特征,获得更好的识别性能。本文还将近年来逐渐受到研究者关注的自适应非平稳信号分析方法——经验模态分解应用于虹膜识别,通过实验分析捕捉到最适合虹膜分类的频带进行虹膜特征编码,取得了理想的识别性能。
本文的主要创新之处在于：
1、提出一种非极坐标展开的虹膜图像归一化方案
传统的虹膜图像归一化方案都是在极坐标下,采用映射的方式将类似环形的虹膜图像沿角度方向展开为固定尺寸的矩形,以实现虹膜图像的尺度不变性和平移不变性。这种方法简易有效,被以往所有的虹膜识别系统所采用。然而。由于虹膜内外圆半径具有较大差异,这一方案在展开的过程中势必进行大量的插值或抽取,改变了原始虹膜图像纹理几何结构和方向信息,而且由于要避开眼睑与睫毛遮挡区域,实际使用的虹膜有效区域是一个狭窄的长方形图像,这对于以往采用局部信息或将二维虹膜图像转化为一维信号进行特征提取的算法影响可能不大,当采用虹膜全局信息或纹理方向信息作为特征的算法,难免会产生不利的影响。为此本文提出一种不需要将其极坐标展开为长条矩形的归一化方法,保留了虹膜图像纹理的原始几何结构和方向信息,利用周边原始虹膜图像反转映射的策略填充内圆中空区域,以免引入新的边界。得到的图像在尺寸上更加均衡,并且可以根据虹膜图像采集的质量选取有效区域,灵活的避开眼睑与睫毛的遮挡,为多尺度几何分析或其他特征提取算法提供了更多的选择。
2、提出一种基于Contourlet变换和支持向量机的虹膜识别系统
多尺度几何分析方法和理论是近年来逐渐兴起的图像处理方法,其在对二维图像的稀疏表示上所展示出的优越性能倍受人们称赞。本文采用一种经典的多尺度几何分析算法——Contourlet变换作为工具提取虹膜图像全局纹理信息,以经过Contourlet变换分解后的多尺度多方向子带的归一化能量作为特征矢量,应用支持向量机进行分类识别,通过实验,本文遍历性的尝试了不同的尺度分解级数和各尺度下不同的分解方向数,列出各种方案下的识别率,为多尺度几何分析在虹膜识别中的进一步应用研究提供了有益的探索和指导。
3、提出一种改进的圆周对称滤波器组应用于虹膜特征提取的方法
多分辨率分析理论经过二十多年的发展,已成为图像处理不可或缺的重要工具,实现图像多分辨率分析的简单有效的结构就是一系列分辨率逐步降低的金字塔形图像集合,对图像不同分辨率下的分析非常有利于模式识别。本文在简要分析比较目前常用金字塔分解算法的基础上,采用一种改进的圆周对称滤波器组用于虹膜图像的多分辨率分解,不仅可以从理论上消除频谱混叠现象,而且具有平移和旋转不变性,使虹膜图像各尺度上的分量更加准确和稳定；并结合方向滤波器组对虹膜图像进行各尺度下的方向分解,获取虹膜多尺度多方向性信息进行识别,实验结果表明,这种方法可以获得更好的识别效果。
4、提出一种基于经验模态分解的虹膜识别算法
经验模态分解是一种新颖的信号分析方法,它可以自适应的将非平稳、非线性信号分解成一组稳态、线性的固有模态函数分量,这些固有模态函数分量相互独立,突出了信号的局部特征,对应着不同的频带信息,从而实现信号的多分辨率分解。本文利用这一特性,将虹膜图像分解为一系列按频率由高到低排列的固有模态函数分量,以互信息测度为初步评判依据,并进一步通过大量实验分析筛选出有利于虹膜识别的固有模态函数分量及其组合,对其进行有效的特征编码,并使用运算量较小的汉明距离匹配算法进行分类识别。这一方法不仅可以简化虹膜图像预处理过程,而且可以达到很高的识别性能。
本文的工作是为了丰富现有虹膜识别算法而进行的一些有益的探索和尝试,实验结果表明,所提出的虹膜识别算法基本满足实际应用的要求,在此基础上开展进一步的研究并加以完善,可以开发出我国具有自主知识产权的虹膜识别系统。
With the development of information technology, the importance of personal authentication is becoming increasingly apparent. Higher and higher requirements are imposed on it from national security to everyone's daily life. For our country with so large a population, identity authentication has broad application prospects and strategic importance. Biometric identification, as an authentication technology with flourishing development at the end of the last century, makes use of a person's physiological and behavior characteristics to determine personal identity. It plays an increasingly important role in our social life and will gradually take the place of the traditional identification methods. Iris recognition is a biometric recognition technology emerging in recent years. It has attracted the attentions of academic and business community for its advantages such as high accuracy, non-contact collection, easy to use etc. It is widely considered as the most promising biometric recognition technology in the future.
Multi-resolution analysis is a very important tool in modern digital image processing. It can represent an image in a multi-scale manner and can be used to find the image characteristics and extract image features unavailable in the pixel domain. With Iris recognition technology as the topic and based on the deeply study on the multi-resolution theory and the main features of iris recognition, this thesis researches on the efficient feature extraction algorithms of Iris images under multi-resolution framework and corresponding preprocessing and feature matching algorithms. Firstly, the principle of biometric recognition and the commonly used biometric recognition methods are introduced briefly, with emphasis on the main features, history, the key components and the current research status of iris recognition technology. Then, after investigating the existing preprocessing methods in depth, a normalization method for the Iris image is proposed which is more suitable for extracting the texture features from the 2D Iris image. Subsequently, based on the Multi-scale Geometric Analysis (MGA) theory, the classic contourlet transform is adopted to extract the multi-scale and multi-directional information as the feature vector, and support vector machine is used to classify these features for recognition. Next, for the deficiency of the Laplacian pyramid in contourlet transform, after comparing the commonly used multi-resolution analysis methods, a superior circular symmetric filter is applied to decompose the Iris image in order to get more stable and accurate Iris features and thus better the recognition performance. In addition, an adaptive non-stationary signal analysis method-empirical mode decomposition is used for Iris recognition, the frequency bands most suitable for recognition are identified through experiments and ideal recognition performance is achieved.
The main innovations of this thesis include:
1. A normalization scheme of the iris image is proposed with non-polar expansion
In traditional methods, the Iris image is normalized under polar coordinate, the annular-like Iris image is mapped into a rectangular region of fixed size along the angular direction, in this way, the scale and translation invariance can be achieved. This method is simple and effective, so it is widely used in all previous Iris recognition systems. However, because of the difference between the radii of inner and outer circles, a large number of interpolation and decimation operations must be taken during the expansion process, the texture structure and directional information of the Iris image are inevitably altered. And what's more, in order to avoid the regions with eyelids and eyelashes occlusion, the available region is just a narrow rectangular image, this may take little effect on the feature extraction methods of using local information or transforming the Iris image into a 1D signal. But surely, it will make a negative impact on the algorithms with global information or directional information as the features. To solve this problem, a normalization method is proposed in which there is no need to expand the polar coordinate into a rectangular. This method can preserve the original geometric structure and directional information of the Iris texture, and a reverse mapping strategy is used to fill in the region in the inner circle avoiding introducing new boundaries. The obtained normalized image is more balanced in size, and the effective region for Iris recognition can be selected flexibly according to the acquisition quality, which provides more choices for the feature extraction methods using multi-scale geometric analysis and other methods.
2. An Iris recognition system based on contourlet transform and SVM is proposed
Multi-scale Geometric Analysis methods have superior performance in sparsely representing the 2D image. In this thesis, contourlet transform is used to extract the global texture information of an Iris image, the normalized energies of the multi-scale and multi-directional subbands act as the feature vector, and the SVM is adopted for recognition. Experiments have been designed to try every possible combination of the decomposition level and number of directions used in an exhaustive manner, and the recognition rates under all schemes are listed, which can provide an instruction for further research on the Iris recognition systems based on MGA.
3. An Iris feature extraction method based on an improved circular symmetric multi-resolution decomposition scheme is proposed
The Multi-resolution analysis theory has become the indispensable tool in image processing community. The analysis process under different resolutions has advantageous for pattern recognition. Based on the comparisons of the classic pyramid decomposition algorithms, an improved circular symmetric filter bank is used for Iris image decomposition. It avoids the frequency aliasing effect and owns translation and rotation invariance, which makes the Iris information in different scales more accurate and stable. Then the directional filter bank is adopted to get the directional information for recognition. Experimental results show that the proposed scheme can achieve better recognition rates.
4. An Iris recognition approach based on empirical mode decomposition is proposed
The empirical mode decomposition(EMD) can adaptively decompose a non-stationary, nonlinear signal into a series of stationary and linear intrinsic mode functions(IMF). These IMFs are independent of each other and reflect the local characteristics of a signal. EMD can be regarded as a multi-resolution decomposition method with each IMF corresponding to a different frequency band. In the proposed method, the Iris image is decomposed by EMD into a few IMFs from high to low frequencies, then the IMFs or their combinations most suitable for recognition are determined with mutual information criterion as a primitive evaluation and large amount of experimental analysis. The selected IMF or IMFs are coded effectively and Hamming distance is adopted as the matching algorithm for recognition. Experiment results show that this method can not only simplify the preprocessing procedure, but can achieve surprisingly high recognition performance.
The work in this thesis can be regarded as some useful exploration and attempt in order to enrich the existing Iris recognition algorithms. Experimental results show that the proposed iris recognition algorithms can basically meet the requirements of practical applications. Based on this work, further deep researches can be conducted to improve the algorithms' performance, and an iris recognition system with independent intellectual property of China is expected to be developed.further research can be carried out based on it.
引文
[1]D. Zhang. Automated Biometrics:Technologies and Systems. Norwell, MA:Kluwer,2000.
[2]J. L. Wayman. Fundamentals of Biometric Authentication Technologies. International Joumal of Image and Graphics, vol.1, no.1, pp.93-113,2001.
[3]王蕴红,谭铁牛.现代身份鉴别新技术——生物特征识别技术.中国基础科学,no.9,pp.4-10，2000.
[4]A. K. Jain, A. Ross, and S. Prabhakar. An Introduction to Biometric Recognition. IEEE Transactions on Circuits and Systems for Video Technology, vol.14, no.1, pp.4-20,2004.
[5]S. Prabhakar, S. Pankanti, and A. K. Jain. Biometric Recognition:Security & Privacy Concerns. IEEE Security & Privacy Magazine, vol.1, no.2, pp.33-42,2003.
[6]A. K. Jain, L. Hong. Bolle, R. On-Line fingerprint verification. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.19, no.4, pp.302-313,1997.
[7]A. K. Jain, S. Prabhakar, L. Hong and S. Pankanti. Filterbank-Based Fingerprint Matching. IEEE Transaction on Image Processing, vol.9, no.5, pp.846-859,2000.
[8]D. Maltoni, D. Maio, A. K. Jain, and S. Prabhakar. Handbook of Fingerprint Recognition. Springer Verlag,2003.
[9]D. Zhang and W. Shu. Two Novel Characteristic in Palmprint Verification:Datum Point Invariance and Line Feature Matching. Pattern Recognition, vol.32, no.4, pp.691-702,1999.
[10]D. Zhang, W. K. Kong, J. You, and M. Wong. Online Palmprint Identification. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.25, no.9, pp.1041-1050, 2003.
[11]S. Chaudhuri. S. Chatterjee, N. Katz, M. Nelson et al. Detection of blood vessels retinal images using two-dimensional matched filters. IEEE Transactions on Medical Imaging, vol.8, no.3, pp.263-269,1989.
[12]H. Tabatabaee, A. Milani Fard, and H. Jafariani. A novel human identifier system using retina image and fuzzy clustering approach. Proceedings of the 2nd IEEE International Conference on Information and Communication Technologies (ICTTA'06), pp.1031-1036, Damascus, Syria, April 2006.
[13]M. Turk, A. Pentland. Eigenfaces for recognition. Journal of Cognitive Neuroscience archive, vol.3, no.1, pp.71-86,1991.
[14]J. Daugman. Face and gesture recognition:overview. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.19, no.7, pp.675-676,1997.
[15]S. Z. Li, A. K. Jain, Handbook of Face Recognition. Springer, New York,2004.
[16]H. Sheikhzadeh, L. Deng. Waveform-based speech recognition using hidden filter models: parameter selection and sensitivity to power normalization. IEEE Transactions on Speech and
Audio Processing, vol.2, no.1, pp.80-89,1994.
[17]J. Campbell. Speaker recognition:A tutorial. Proceedings of the IEEE, vol.85, no.9, pp. 1437-1462,1997.
[18]D. A. Reynolds, T. F. Quatieri, R. B. Dunn. Speaker verification using adapted Gaussian mixture models. Digital Signal Processing, vol.10, no.1-3, pp.19-41,2000.
[19]R. Plamondon, S. N. Srihari. On-Line and Off-Line Handwriting Recognition:A Comprehensive Survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no.1, pp.63-84,2000.
[20]C. Bahlmann, H. Burkhardt. The writer independent online handwriting recognition system frog on hand and cluster generative statistical dynamic time warping. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.26, no.3, pp.229-310,2004.
[21]N. V. Boulgouris, D. Hatzinakos, K. N. Plataniotis. Gait recognition:a challenging signal processing technology for biometric identification. IEEE Signal Processing Magazine, vol.22, no.6, pp.78-90,2005.
[22]Z. Liu, S. Sarkar. Improved gait recognition by gait dynamics normalization. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.28, no.6, pp.863-876,2006.
[23]S. Yu, D. Tan, T. Tan. A Framework for Evaluating the Effect of View Angle, Clothing and Carrying Condition on Gait Recognition. International Conference on Pattern Recognition (ICPR'06), vol.4, pp.441-444,2006.
[24]Z. Ji, Y. Jian. The development of automatic recognition system for DNA, Engineering in Medicine and Biology Society. Proceedings of the Annual International Conference of the IEEE, pp.366-367, Nov.1988.
[25]E. Ficarra, L. Benini, E. Macii, G. Zuccheri. Automated DNA fragments recognition and sizing through AFM image processing. IEEE Transactions on Information Technology in Biomedicine, vol.9, no.4, pp.508-517,2005.
[26]L. Hong, A. Jain. Multimodal Biometrics. Biometrics Personal Identification in Networked Society, Springer US, pp.327-344,1999
[27]L. Flom, A. Safir. Iris Recognition System. United States Patent, No.4641349,1987.
[28]T. Mansfield, G. Kelly, D. Chandler, and J. Kane. Biometric Product Testing Final Report, Issue 1.0. National Physical Laboratory of UK,2001.
[29]郭秉宽.眼科学.上海科学技术出版社,上海,2008.
[30]J. Rohen. Morphology and pathology of the trabecular meshwork. The Structure of the Eye, Smelser, New York:Academic Press, pp.335-341,1961.
[31]J. Daugman. High Confidence Visual Recognition of persons by a Test of Statistical Independence. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.15, no. 11, pp.1148-1161,1993.
[32]F. H. Adler. Physiology of the Eye:Clinical Application, Fourth edition. The C. V. Mosby Company, London,1965.
[33]I. Mann. The Development of the Human Eye. Grime and Stratton, NewYork,1950.
[34]P. C. Kronfeld. The Cross Anatomy and the Embryology of the Eye. in The Eye, Academic, London, pp.1-66,1962.
[35]J. Daugman. Biometric Personal Identification System Based on Iris Analysis. United States Patent, No.5291560,1994.
[36]R. Wildes, J. Asmuth, K. Hanna, S. Hsu, R. Kolczynski, J. Matey, S. McBride. Automated non-invasive iris recognition system and method. United States Patent, No.5751836,1996.
[37]J. Daugman. Fast focus assessment system and method for imaging. United States Patent, No. 6753919,2004.
[38]J. Daugman. How iris recognition works. IEEE Transactions on Circuits and Systems for Video Technology, vol.14, no.1, pp.21-30,2004.
[39]R. Wildes, J. Asmuth, G. L. Green, S. C. Hsu et al. A mashine-vision system for iris recognition. Machine Vision and Application, vol.9, no.1, pp.1-8,1996.
[40]谭铁牛,朱勇,王蕴红.虹膜图像采集装置.实用新型专利,专利号：CN2392219,1999.
[41]谭铁牛,王蕴红,马力.活体虹膜图像采集装置.实用新型专利,专利号：CN2508306,2001.
[42]宫雅卓,沈文忠.非接触式自反馈虹膜图像采集装置.发明专利,专利号：CN1664848,2005.
[43]R. Wildes. Iris recognition:An emerging biometric technology. Proceedings of the IEEE, vol. 85, no.9, pp.1348-1363,1997.
[44]N. Ritter, R. Owens, J. Cooper, P. P. Van Saarloos. Location of the pupil-iris border in slit-lamp images of the cornea. Proceedings of Conference on Image Analysis and Processing, 740-745,1999.
[45]W. Boles and B. Boashash. A human identification technique using images of the iris and wavelet transform. IEEE Transactions on Signal Processing, vol.46, no.4, pp.1185-1188, 1998.
[46]L. Ma, T. Tan, Y. Wang, D. Zhang. Personal Recognition Based on Iris Texture Analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. no.12, pp.1519-1533, 2003.
[47]王蕴红,朱勇,谭铁牛.基于虹膜识别的身份鉴别.自动化学报,vol.28,no.1,pp.1-10,2002.
[48]A. Bertillon, La couleur de L'Iris. Review of of Science, vol.36, no.3, pp.65-73,1885.
[49]Internatinal Biometrics Group. Iris Recognition. http://www.biometrics.gov/Documents/irisrec.pdf
[50]J. Daugman. New Methods in Iris Recognition. IEEE Transactions on Systems, Man, and Cybernetics. Vol.37, no.5,1167-1175,2007.
[51]R. Wildes, J. Asmuth, G. L. Green, S. C. Hsu et al. A system for automated iris recognition. Proceedings of the Second IEEE Workshop on Applications of Computer Vision, pp.121-128, 1994.
[52]L. Ma, T. Tan, Y. Wang, D. Zhang. Efficient iris recognition by characterizing key local variations. IEEE Transactions on Image Processing, vol.13, no.6, pp.739-750,2004.
[53]Internatinal Biometrics Group. Biometrics Market and Industry Report 2009-2014. http://www.biometricgroup.com/reports/public/market report.php
[54]Internatinal Biometrics Group. Iris Recognition Technology Market http://www.biometricgroup.com/reports/public/reports/iris-scan market.html
[55]J. P. Ivins, J. Porrill, J. P. Frisby. A deformable model of the human iris driven by non-linearleast-squares minimisation. The 6th International Conference on Image Processing and its Applications, vol.1, no.14-17, pp.234-238,1997.
[56]Harry J. Wyatt. A'minimum-wear-and-tear'meshwork for the iris. Vision Research, vol.40, no.16, pp.2167-2176,2000.
[57]Xiaoyan Yuan, Pengfei Shi. A Non-linear Normalization Model for Iris Recognition. Lecture Notes in Computer Science, Springer Berlin/Heidelberg, vol.3781, pp.135-141,2005.
[58]Zhuoshi Wei, Tieniu Tan, Zhenan Sun. Nonlinear Iris Deformation Correction Based on Gaussian Model. Lecture Notes in Computer Science, Springer Berlin/Heidelberg, vol.4642, pp.780-790,2007.
[59]S. Mallat. A wavelet tour of signal processing. Boston:Academic,1998.
[60]S. Mallat. A theory for multiresolution signal decomposition:The wavelet representation. IEEE Transaction on Pattern Analysis and Machine Intelligence, vol.11, no.7, pp.674-693, 1989.
[61]彭玉华.小波变换与工程应用.科学出版社,北京,1999.
[62]焦李成,谭山.图像的多尺度几何分析：回顾和展望.电子学报,vol.31,no.12A,pp.1975-1981，2003.
[63]C. H. Sulochanal, S. Selvan. Iris recognition based on contourlet transform and dominant energy features. Advanced Modeling and Optimization, vol.9, no.2, pp.219-236,2007.
[64]M. N. Do, M. Vetterli. The contourlet transform:an efficient directional multiresolution image representation. IEEE Transactions Image on Processing, vol.14, no.12, pp.2091-2106, Dec. 2005.
[65]焦李成等.图像多尺度几何分析理论与应用,西安电子科技大学出版社,西安,2008.
[66]E. J. Candes. Ridgelets:Theory and applications. Ph.D. thesis, Department of Statistics, Stanford University,1998.
[67]M. N. Do, M. Vetterli. The finite ridgelet transform for image representation. IEEE Transactions on Image Processing, vol.12, no.1, pp.16-28,2003.
[68]J. L. Starck, E. J. Candes, D. L. Donoho. Astronomical image representation by the curvelet transform. Astronomy and Astrophysics vol.398, no.2, pp.785-800,2003.
[69]J. L. Starck, E. J. Candes, D. L. Dbnohb. The curvelet transform for image denoising. IEEE Transactions on Image Processing, vol.11, no.6, pp.670-684,2002.
[70]E. Le Pennec, S. Mallat. Sparse geometric image representations with bandelets. IEEE Transactions on Image Processing, vol.14, no.4, pp.423-438,2005.
[71]X. H. Yang, W. Li, L. C. Jiao. Image denoising based on second generation bandelets and multi-level thresholding. In Proceedings of WCICA'06, vol.2, pp.9509-9513,2006.
[72]D. L. Donoho. Wedgelets:nearly-minimax estimation of edges. Annals of Statistics, vol.27, no.3, pp.859-897,1999.
[73]M. B. Wakin, J. K. Romberg, H. Choi. R. G. Baraniuk. Rate-distortion optimized image compression using wedgelets. In proceedings of ICIP'02, vol.3, pp. Ⅲ_237-Ⅲ_240,2002.
[74]D. D.-Y. Po, M. N. Do. Directional multiscale modeling of images using the contourlet transform. IEEE Transactions on Image Processing, vol.15, no.6, pp.1610-1620,2006.
[75]R. Eslami, H. Radha. Translation-invariant contourlet transform and its Application to image denoising. IEEE Transactions on Image Processing, vol.15, no.11, pp.3362-3374,2006.
[76]A. L. Cunha, J. Zhou, M. N. Do. The nonsubsampled contourlet transform:Theory, design, and applications. IEEE Transactions on Image Processing, vol.15, no.10, pp.3089-3101, 2006.
[77]林立宇等,CONTOURLET变换——影像处理应用,科学出版社,北京,2008.
[78]David Marr. Vision:A computational investigation into the human representation and processing of visual Information. San Francisco:W H Freeman,1982
[79]P. J. Burt, E. H. Adelson. The Laplacian pyramid as a compact image code. IEEE Transactions on Communications, vol.31, no.4, pp.532-540,1983.
[80]M. N. Do, M. Vetterli. Framing pyramids. IEEE Transactions on Signal processing, vol.51, no. 9, pp.2329-2342,2003.
[81]R. H. Bamberger, M. J. T. Smith. A filter bank for the directional decomposition of images: Theory and design. IEEE Transaction on Signal Processing, vol.40, no.4, pp.882-893,1992.
[82]M. N. Do. Directional multiresolution image representations. Ph.D. thesis, Department of Communication Systems, Swiss Federal Institute of Technology, Lausanne,2001.
[83]V. Vapnik, Statistical Learning Theory. Wiley, New York,1998.
[84]S. Li, J. T. Kwok, H. Zhu and Y. Wang. Texture classification using the support vector machines. Pattern Recognition, vol.36, pp.2883-2893,2003.
[85]K. I. Kim, K. Jung, S. H. Park and H. J. Kim. Support vector machines for texture classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.24, no. 11, pp.2883-2893,2002.
[86]X. Qin and Y. Yang. Simiarity measure and learning with gray level aura matrices (GLAM) for texture image retrieval. In Proceedings of IEEE Computer Society Conference on
Computer Vision and Pattern Recognition, vol.1, pp.326-333, 2004.
[87]C. Chih-Chung and L. Chi-Jen:LIBSVM:A Library for Support Vector Machines.2004.
[88]中国科学院自动化研究所生物识别与信息安全技术研究中心.CASIA-IrisV3. http://www.sinobiometrics.com/english/IrisDatabase.asp
[89]冯鹏,魏彪,潘英俊,米德伶.基于拉普拉斯塔型变换的Contourlet变换频谱混叠特性分析.光学学报,vol.28,no.11,pp.2090-2096,2008.
[90]A. L. da Cunha, J. Zhou, M. N. Do. The Nonsubsampled Contourlet Transform:Theory, Design and Applications. IEEE Transactions on Image Processing, vol.15, no.10, pp. 3089-3101,2006.
[91]G. V. Welland ed.. Beyond Wavelets, Academic Press,2003.
[92]P. J. Burt, A. E. Adelson. The Laplacian pyramid as a compact image code. IEEE Transactions on Communications, vol.31, no.4, pp.532-540,1983.
[93]章毓晋.图像工程(第2版).清华大学出版社,北京,2007.
[94]E. P. Simoncelli, W.T. Freeman, E.H. Adelson, et al. Shiftable multiscale transforms. IEEE Transactions on Information Theory, vol.38, no.2, pp.587-607,1992.
[95]E. P. Simoncelli, W.T. Freeman. The steerable pyramid:a flexible architecture for multiscale derivative computation. The 2nd Annual International Conference on Image Processing, Washington, DC, pp.444-447,1995.
[96]练秋生,孔令富.圆对称轮廓波变换的构造.计算机学报,vol.29,no.4,pp.652-657,2006.
[97]练秋生,孔令富.具有多方向选择性的小波构造.电子学报,vol.10,no.33,pp.1905-1909,2005.
[98]练秋生.基于视觉特性的多方向小波构造及其应用研究.(博士学位论文),燕山大学,2006.
[99]李晖,彭玉华,尹勇.基于平移旋转不变的塔式分解和模糊梯度场的医学图像配准.电子学报,vol.37,no.4.pp.854-859,2009.
[100]J. H. McClellan, T. W. Parks. A unified approach to the design of optimum FIR linear phase digital filters. IEEE Transactions on Circuit Theory, vol.20, pp.697-701,1973.
[101]J. H. McClellan, T. W. Parks, and L. R. Rabiner. A computer program for designing optimum FIR linear phase digital filters. IEEE Transactions on Audio and Electroacoust, vol.21, pp. 506-526,1973.
[102]何振亚.多维数字信号处理.国防工业出版社,北京,1995.
[103]N. E. Huang, Z. Shen, S. R. Long, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London, vol. A(454), pp.903-995,1998.
[104]C. P. Chang, J. C. Lee, Y. Su, P. S. Huang. Using empirical mode decomposition for iris recognition. Computer Standards & Interfaces, vol.31, no.4, pp.729-739,2009.
[105]N. E. Huang, Z. Shen, and S. R. Long:A new review of nonlinear water waves:The Hilbert Spectrum. Annual Review of Fluid Mechanics, vol.31, pp.417-457,1997.
[106]P. Flandrin, G. Rilling and P. Goncalves. Empirical Mode Decomposition as a filter Bank. IEEE Signal Processing Letters, vol.11, no.2, pp.112-114,2004.
[107]F. T. Wang, S. H. Chang, C. Y. Lee. Hybrid wavelet-Hilbert-Huang spectrum analysis. Oceans 2005 Europe IEEE Conference and Exhibition, vol.2, pp.902-905,2005.
[108]C. H. Loh, T. C. Wu, N. E.Huang. Application of the empirical mode decomposition-Hilbert spectrum method to identify near-fault ground-motion characteristics and structural responses. Bulletin of the seismological Society of America, vol.91, no.5, pp.1339-1357,2001.
[109]Z. K. Peng, P. W. Tse, F. L. Chu. A comparison study of improved Hilbert-Huang transform and wavelet transform:Application to fault diagnosis for rolling bearing. Mechanical systems and signal processing, vol.19, no.5, pp.974-988,2005.
[110]苑玮琦,白云,柯丽.虹膜区域选取与识别率对应关系分析.光学学报,vol.28,no.5,pp.937-942,2008.
[2]J. L. Wayman. Fundamentals of Biometric Authentication Technologies. International Joumal of Image and Graphics, vol.1, no.1, pp.93-113,2001.
[3]王蕴红,谭铁牛.现代身份鉴别新技术——生物特征识别技术.中国基础科学,no.9,pp.4-10，2000.
[4]A. K. Jain, A. Ross, and S. Prabhakar. An Introduction to Biometric Recognition. IEEE Transactions on Circuits and Systems for Video Technology, vol.14, no.1, pp.4-20,2004.
[5]S. Prabhakar, S. Pankanti, and A. K. Jain. Biometric Recognition:Security & Privacy Concerns. IEEE Security & Privacy Magazine, vol.1, no.2, pp.33-42,2003.
[6]A. K. Jain, L. Hong. Bolle, R. On-Line fingerprint verification. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.19, no.4, pp.302-313,1997.
[7]A. K. Jain, S. Prabhakar, L. Hong and S. Pankanti. Filterbank-Based Fingerprint Matching. IEEE Transaction on Image Processing, vol.9, no.5, pp.846-859,2000.
[8]D. Maltoni, D. Maio, A. K. Jain, and S. Prabhakar. Handbook of Fingerprint Recognition. Springer Verlag,2003.
[9]D. Zhang and W. Shu. Two Novel Characteristic in Palmprint Verification:Datum Point Invariance and Line Feature Matching. Pattern Recognition, vol.32, no.4, pp.691-702,1999.
[10]D. Zhang, W. K. Kong, J. You, and M. Wong. Online Palmprint Identification. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.25, no.9, pp.1041-1050, 2003.
[11]S. Chaudhuri. S. Chatterjee, N. Katz, M. Nelson et al. Detection of blood vessels retinal images using two-dimensional matched filters. IEEE Transactions on Medical Imaging, vol.8, no.3, pp.263-269,1989.
[12]H. Tabatabaee, A. Milani Fard, and H. Jafariani. A novel human identifier system using retina image and fuzzy clustering approach. Proceedings of the 2nd IEEE International Conference on Information and Communication Technologies (ICTTA'06), pp.1031-1036, Damascus, Syria, April 2006.
[13]M. Turk, A. Pentland. Eigenfaces for recognition. Journal of Cognitive Neuroscience archive, vol.3, no.1, pp.71-86,1991.
[14]J. Daugman. Face and gesture recognition:overview. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.19, no.7, pp.675-676,1997.
[15]S. Z. Li, A. K. Jain, Handbook of Face Recognition. Springer, New York,2004.
[16]H. Sheikhzadeh, L. Deng. Waveform-based speech recognition using hidden filter models: parameter selection and sensitivity to power normalization. IEEE Transactions on Speech and
Audio Processing, vol.2, no.1, pp.80-89,1994.
[17]J. Campbell. Speaker recognition:A tutorial. Proceedings of the IEEE, vol.85, no.9, pp. 1437-1462,1997.
[18]D. A. Reynolds, T. F. Quatieri, R. B. Dunn. Speaker verification using adapted Gaussian mixture models. Digital Signal Processing, vol.10, no.1-3, pp.19-41,2000.
[19]R. Plamondon, S. N. Srihari. On-Line and Off-Line Handwriting Recognition:A Comprehensive Survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no.1, pp.63-84,2000.
[20]C. Bahlmann, H. Burkhardt. The writer independent online handwriting recognition system frog on hand and cluster generative statistical dynamic time warping. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.26, no.3, pp.229-310,2004.
[21]N. V. Boulgouris, D. Hatzinakos, K. N. Plataniotis. Gait recognition:a challenging signal processing technology for biometric identification. IEEE Signal Processing Magazine, vol.22, no.6, pp.78-90,2005.
[22]Z. Liu, S. Sarkar. Improved gait recognition by gait dynamics normalization. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.28, no.6, pp.863-876,2006.
[23]S. Yu, D. Tan, T. Tan. A Framework for Evaluating the Effect of View Angle, Clothing and Carrying Condition on Gait Recognition. International Conference on Pattern Recognition (ICPR'06), vol.4, pp.441-444,2006.
[24]Z. Ji, Y. Jian. The development of automatic recognition system for DNA, Engineering in Medicine and Biology Society. Proceedings of the Annual International Conference of the IEEE, pp.366-367, Nov.1988.
[25]E. Ficarra, L. Benini, E. Macii, G. Zuccheri. Automated DNA fragments recognition and sizing through AFM image processing. IEEE Transactions on Information Technology in Biomedicine, vol.9, no.4, pp.508-517,2005.
[26]L. Hong, A. Jain. Multimodal Biometrics. Biometrics Personal Identification in Networked Society, Springer US, pp.327-344,1999
[27]L. Flom, A. Safir. Iris Recognition System. United States Patent, No.4641349,1987.
[28]T. Mansfield, G. Kelly, D. Chandler, and J. Kane. Biometric Product Testing Final Report, Issue 1.0. National Physical Laboratory of UK,2001.
[29]郭秉宽.眼科学.上海科学技术出版社,上海,2008.
[30]J. Rohen. Morphology and pathology of the trabecular meshwork. The Structure of the Eye, Smelser, New York:Academic Press, pp.335-341,1961.
[31]J. Daugman. High Confidence Visual Recognition of persons by a Test of Statistical Independence. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.15, no. 11, pp.1148-1161,1993.
[32]F. H. Adler. Physiology of the Eye:Clinical Application, Fourth edition. The C. V. Mosby Company, London,1965.
[33]I. Mann. The Development of the Human Eye. Grime and Stratton, NewYork,1950.
[34]P. C. Kronfeld. The Cross Anatomy and the Embryology of the Eye. in The Eye, Academic, London, pp.1-66,1962.
[35]J. Daugman. Biometric Personal Identification System Based on Iris Analysis. United States Patent, No.5291560,1994.
[36]R. Wildes, J. Asmuth, K. Hanna, S. Hsu, R. Kolczynski, J. Matey, S. McBride. Automated non-invasive iris recognition system and method. United States Patent, No.5751836,1996.
[37]J. Daugman. Fast focus assessment system and method for imaging. United States Patent, No. 6753919,2004.
[38]J. Daugman. How iris recognition works. IEEE Transactions on Circuits and Systems for Video Technology, vol.14, no.1, pp.21-30,2004.
[39]R. Wildes, J. Asmuth, G. L. Green, S. C. Hsu et al. A mashine-vision system for iris recognition. Machine Vision and Application, vol.9, no.1, pp.1-8,1996.
[40]谭铁牛,朱勇,王蕴红.虹膜图像采集装置.实用新型专利,专利号：CN2392219,1999.
[41]谭铁牛,王蕴红,马力.活体虹膜图像采集装置.实用新型专利,专利号：CN2508306,2001.
[42]宫雅卓,沈文忠.非接触式自反馈虹膜图像采集装置.发明专利,专利号：CN1664848,2005.
[43]R. Wildes. Iris recognition:An emerging biometric technology. Proceedings of the IEEE, vol. 85, no.9, pp.1348-1363,1997.
[44]N. Ritter, R. Owens, J. Cooper, P. P. Van Saarloos. Location of the pupil-iris border in slit-lamp images of the cornea. Proceedings of Conference on Image Analysis and Processing, 740-745,1999.
[45]W. Boles and B. Boashash. A human identification technique using images of the iris and wavelet transform. IEEE Transactions on Signal Processing, vol.46, no.4, pp.1185-1188, 1998.
[46]L. Ma, T. Tan, Y. Wang, D. Zhang. Personal Recognition Based on Iris Texture Analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. no.12, pp.1519-1533, 2003.
[47]王蕴红,朱勇,谭铁牛.基于虹膜识别的身份鉴别.自动化学报,vol.28,no.1,pp.1-10,2002.
[48]A. Bertillon, La couleur de L'Iris. Review of of Science, vol.36, no.3, pp.65-73,1885.
[49]Internatinal Biometrics Group. Iris Recognition. http://www.biometrics.gov/Documents/irisrec.pdf
[50]J. Daugman. New Methods in Iris Recognition. IEEE Transactions on Systems, Man, and Cybernetics. Vol.37, no.5,1167-1175,2007.
[51]R. Wildes, J. Asmuth, G. L. Green, S. C. Hsu et al. A system for automated iris recognition. Proceedings of the Second IEEE Workshop on Applications of Computer Vision, pp.121-128, 1994.
[52]L. Ma, T. Tan, Y. Wang, D. Zhang. Efficient iris recognition by characterizing key local variations. IEEE Transactions on Image Processing, vol.13, no.6, pp.739-750,2004.
[53]Internatinal Biometrics Group. Biometrics Market and Industry Report 2009-2014. http://www.biometricgroup.com/reports/public/market report.php
[54]Internatinal Biometrics Group. Iris Recognition Technology Market http://www.biometricgroup.com/reports/public/reports/iris-scan market.html
[55]J. P. Ivins, J. Porrill, J. P. Frisby. A deformable model of the human iris driven by non-linearleast-squares minimisation. The 6th International Conference on Image Processing and its Applications, vol.1, no.14-17, pp.234-238,1997.
[56]Harry J. Wyatt. A'minimum-wear-and-tear'meshwork for the iris. Vision Research, vol.40, no.16, pp.2167-2176,2000.
[57]Xiaoyan Yuan, Pengfei Shi. A Non-linear Normalization Model for Iris Recognition. Lecture Notes in Computer Science, Springer Berlin/Heidelberg, vol.3781, pp.135-141,2005.
[58]Zhuoshi Wei, Tieniu Tan, Zhenan Sun. Nonlinear Iris Deformation Correction Based on Gaussian Model. Lecture Notes in Computer Science, Springer Berlin/Heidelberg, vol.4642, pp.780-790,2007.
[59]S. Mallat. A wavelet tour of signal processing. Boston:Academic,1998.
[60]S. Mallat. A theory for multiresolution signal decomposition:The wavelet representation. IEEE Transaction on Pattern Analysis and Machine Intelligence, vol.11, no.7, pp.674-693, 1989.
[61]彭玉华.小波变换与工程应用.科学出版社,北京,1999.
[62]焦李成,谭山.图像的多尺度几何分析：回顾和展望.电子学报,vol.31,no.12A,pp.1975-1981，2003.
[63]C. H. Sulochanal, S. Selvan. Iris recognition based on contourlet transform and dominant energy features. Advanced Modeling and Optimization, vol.9, no.2, pp.219-236,2007.
[64]M. N. Do, M. Vetterli. The contourlet transform:an efficient directional multiresolution image representation. IEEE Transactions Image on Processing, vol.14, no.12, pp.2091-2106, Dec. 2005.
[65]焦李成等.图像多尺度几何分析理论与应用,西安电子科技大学出版社,西安,2008.
[66]E. J. Candes. Ridgelets:Theory and applications. Ph.D. thesis, Department of Statistics, Stanford University,1998.
[67]M. N. Do, M. Vetterli. The finite ridgelet transform for image representation. IEEE Transactions on Image Processing, vol.12, no.1, pp.16-28,2003.
[68]J. L. Starck, E. J. Candes, D. L. Donoho. Astronomical image representation by the curvelet transform. Astronomy and Astrophysics vol.398, no.2, pp.785-800,2003.
[69]J. L. Starck, E. J. Candes, D. L. Dbnohb. The curvelet transform for image denoising. IEEE Transactions on Image Processing, vol.11, no.6, pp.670-684,2002.
[70]E. Le Pennec, S. Mallat. Sparse geometric image representations with bandelets. IEEE Transactions on Image Processing, vol.14, no.4, pp.423-438,2005.
[71]X. H. Yang, W. Li, L. C. Jiao. Image denoising based on second generation bandelets and multi-level thresholding. In Proceedings of WCICA'06, vol.2, pp.9509-9513,2006.
[72]D. L. Donoho. Wedgelets:nearly-minimax estimation of edges. Annals of Statistics, vol.27, no.3, pp.859-897,1999.
[73]M. B. Wakin, J. K. Romberg, H. Choi. R. G. Baraniuk. Rate-distortion optimized image compression using wedgelets. In proceedings of ICIP'02, vol.3, pp. Ⅲ_237-Ⅲ_240,2002.
[74]D. D.-Y. Po, M. N. Do. Directional multiscale modeling of images using the contourlet transform. IEEE Transactions on Image Processing, vol.15, no.6, pp.1610-1620,2006.
[75]R. Eslami, H. Radha. Translation-invariant contourlet transform and its Application to image denoising. IEEE Transactions on Image Processing, vol.15, no.11, pp.3362-3374,2006.
[76]A. L. Cunha, J. Zhou, M. N. Do. The nonsubsampled contourlet transform:Theory, design, and applications. IEEE Transactions on Image Processing, vol.15, no.10, pp.3089-3101, 2006.
[77]林立宇等,CONTOURLET变换——影像处理应用,科学出版社,北京,2008.
[78]David Marr. Vision:A computational investigation into the human representation and processing of visual Information. San Francisco:W H Freeman,1982
[79]P. J. Burt, E. H. Adelson. The Laplacian pyramid as a compact image code. IEEE Transactions on Communications, vol.31, no.4, pp.532-540,1983.
[80]M. N. Do, M. Vetterli. Framing pyramids. IEEE Transactions on Signal processing, vol.51, no. 9, pp.2329-2342,2003.
[81]R. H. Bamberger, M. J. T. Smith. A filter bank for the directional decomposition of images: Theory and design. IEEE Transaction on Signal Processing, vol.40, no.4, pp.882-893,1992.
[82]M. N. Do. Directional multiresolution image representations. Ph.D. thesis, Department of Communication Systems, Swiss Federal Institute of Technology, Lausanne,2001.
[83]V. Vapnik, Statistical Learning Theory. Wiley, New York,1998.
[84]S. Li, J. T. Kwok, H. Zhu and Y. Wang. Texture classification using the support vector machines. Pattern Recognition, vol.36, pp.2883-2893,2003.
[85]K. I. Kim, K. Jung, S. H. Park and H. J. Kim. Support vector machines for texture classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.24, no. 11, pp.2883-2893,2002.
[86]X. Qin and Y. Yang. Simiarity measure and learning with gray level aura matrices (GLAM) for texture image retrieval. In Proceedings of IEEE Computer Society Conference on
Computer Vision and Pattern Recognition, vol.1, pp.326-333, 2004.
[87]C. Chih-Chung and L. Chi-Jen:LIBSVM:A Library for Support Vector Machines.2004.
[88]中国科学院自动化研究所生物识别与信息安全技术研究中心.CASIA-IrisV3. http://www.sinobiometrics.com/english/IrisDatabase.asp
[89]冯鹏,魏彪,潘英俊,米德伶.基于拉普拉斯塔型变换的Contourlet变换频谱混叠特性分析.光学学报,vol.28,no.11,pp.2090-2096,2008.
[90]A. L. da Cunha, J. Zhou, M. N. Do. The Nonsubsampled Contourlet Transform:Theory, Design and Applications. IEEE Transactions on Image Processing, vol.15, no.10, pp. 3089-3101,2006.
[91]G. V. Welland ed.. Beyond Wavelets, Academic Press,2003.
[92]P. J. Burt, A. E. Adelson. The Laplacian pyramid as a compact image code. IEEE Transactions on Communications, vol.31, no.4, pp.532-540,1983.
[93]章毓晋.图像工程(第2版).清华大学出版社,北京,2007.
[94]E. P. Simoncelli, W.T. Freeman, E.H. Adelson, et al. Shiftable multiscale transforms. IEEE Transactions on Information Theory, vol.38, no.2, pp.587-607,1992.
[95]E. P. Simoncelli, W.T. Freeman. The steerable pyramid:a flexible architecture for multiscale derivative computation. The 2nd Annual International Conference on Image Processing, Washington, DC, pp.444-447,1995.
[96]练秋生,孔令富.圆对称轮廓波变换的构造.计算机学报,vol.29,no.4,pp.652-657,2006.
[97]练秋生,孔令富.具有多方向选择性的小波构造.电子学报,vol.10,no.33,pp.1905-1909,2005.
[98]练秋生.基于视觉特性的多方向小波构造及其应用研究.(博士学位论文),燕山大学,2006.
[99]李晖,彭玉华,尹勇.基于平移旋转不变的塔式分解和模糊梯度场的医学图像配准.电子学报,vol.37,no.4.pp.854-859,2009.
[100]J. H. McClellan, T. W. Parks. A unified approach to the design of optimum FIR linear phase digital filters. IEEE Transactions on Circuit Theory, vol.20, pp.697-701,1973.
[101]J. H. McClellan, T. W. Parks, and L. R. Rabiner. A computer program for designing optimum FIR linear phase digital filters. IEEE Transactions on Audio and Electroacoust, vol.21, pp. 506-526,1973.
[102]何振亚.多维数字信号处理.国防工业出版社,北京,1995.
[103]N. E. Huang, Z. Shen, S. R. Long, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London, vol. A(454), pp.903-995,1998.
[104]C. P. Chang, J. C. Lee, Y. Su, P. S. Huang. Using empirical mode decomposition for iris recognition. Computer Standards & Interfaces, vol.31, no.4, pp.729-739,2009.
[105]N. E. Huang, Z. Shen, and S. R. Long:A new review of nonlinear water waves:The Hilbert Spectrum. Annual Review of Fluid Mechanics, vol.31, pp.417-457,1997.
[106]P. Flandrin, G. Rilling and P. Goncalves. Empirical Mode Decomposition as a filter Bank. IEEE Signal Processing Letters, vol.11, no.2, pp.112-114,2004.
[107]F. T. Wang, S. H. Chang, C. Y. Lee. Hybrid wavelet-Hilbert-Huang spectrum analysis. Oceans 2005 Europe IEEE Conference and Exhibition, vol.2, pp.902-905,2005.
[108]C. H. Loh, T. C. Wu, N. E.Huang. Application of the empirical mode decomposition-Hilbert spectrum method to identify near-fault ground-motion characteristics and structural responses. Bulletin of the seismological Society of America, vol.91, no.5, pp.1339-1357,2001.
[109]Z. K. Peng, P. W. Tse, F. L. Chu. A comparison study of improved Hilbert-Huang transform and wavelet transform:Application to fault diagnosis for rolling bearing. Mechanical systems and signal processing, vol.19, no.5, pp.974-988,2005.
[110]苑玮琦,白云,柯丽.虹膜区域选取与识别率对应关系分析.光学学报,vol.28,no.5,pp.937-942,2008.
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